1 code implementation • ECCV 2020 • Guolei Sun, Salman Khan, Wen Li, Hisham Cholakkal, Fahad Shahbaz Khan, Luc van Gool
This way, in an effort to fix localization errors, our loss provides an extra supervisory signal that helps the model to better discriminate between similar classes.
no code implementations • ECCV 2020 • Jin Xie, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao, Mubarak Shah
We further introduce a count-and-similarity branch within the two-stage detection framework, which predicts pedestrian count as well as proposal similarity.
no code implementations • 6 May 2024 • Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Jameel Hassan, Muzammal Naseer, Federico Tombari, Fahad Shahbaz Khan, Salman Khan
Recent advancements in Large Language Models (LLMs) have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding tasks.
no code implementations • 23 Apr 2024 • Wenjin Hou, Shiming Chen, Shuhuang Chen, Ziming Hong, Yan Wang, Xuetao Feng, Salman Khan, Fahad Shahbaz Khan, Xinge You
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL.
1 code implementation • 15 Apr 2024 • Amaya Dharmasiri, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
Thereby we demonstrate that 2D vision language models such as CLIP can be used to complement 3D representation learning to improve classification performance without the need for expensive class annotations.
no code implementations • 11 Apr 2024 • Shiming Chen, Wenjin Hou, Salman Khan, Fahad Shahbaz Khan
ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information.
1 code implementation • 2 Apr 2024 • Akshay Dudhane, Omkar Thawakar, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming-Hsuan Yang
All-in-one image restoration tackles different types of degradations with a unified model instead of having task-specific, non-generic models for each degradation.
1 code implementation • 1 Apr 2024 • Shahina Kunhimon, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
Incorporating text features alongside visual features is a potential solution to enhance the model's understanding of the data, as it goes beyond pixel-level information to provide valuable context.
1 code implementation • 26 Mar 2024 • Abdelrahman Shaker, Syed Talal Wasim, Martin Danelljan, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan
Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation.
1 code implementation • 26 Mar 2024 • Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal, Salman Khan, Fahad Shahbaz Khan
Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at distinct time stamps.
1 code implementation • 25 Mar 2024 • Omkar Thawakar, Muzammal Naseer, Rao Muhammad Anwer, Salman Khan, Michael Felsberg, Mubarak Shah, Fahad Shahbaz Khan
Composed video retrieval (CoVR) is a challenging problem in computer vision which has recently highlighted the integration of modification text with visual queries for more sophisticated video search in large databases.
1 code implementation • 21 Mar 2024 • Hasindri Watawana, Kanchana Ranasinghe, Tariq Mahmood, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn better representations.
no code implementations • 21 Mar 2024 • Ahmad Mahmood, Ashmal Vayani, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
In contrast, this paper introduces a Video Understanding and Reasoning Framework (VURF) based on the reasoning power of LLMs.
1 code implementation • 21 Mar 2024 • Yuning Cui, Syed Waqas Zamir, Salman Khan, Alois Knoll, Mubarak Shah, Fahad Shahbaz Khan
Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task.
1 code implementation • 8 Mar 2024 • Mubashir Noman, Muzammal Naseer, Hisham Cholakkal, Rao Muhammad Anwar, Salman Khan, Fahad Shahbaz Khan
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data.
1 code implementation • 7 Mar 2024 • Hashmat Shadab Malik, Muhammad Huzaifa, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
We produce various versions of standard vision datasets (ImageNet, COCO), incorporating either diverse and realistic backgrounds into the images or introducing color, texture, and adversarial changes in the background.
no code implementations • 7 Mar 2024 • Yao Jiang, Xinyu Yan, Ge-Peng Ji, Keren Fu, Meijun Sun, Huan Xiong, Deng-Ping Fan, Fahad Shahbaz Khan
This paper endeavors to evaluate the competency of popular LVLMs in specialized and general tasks, respectively, aiming to offer a comprehensive understanding of these novel models.
1 code implementation • 26 Feb 2024 • Omkar Thawakar, Ashmal Vayani, Salman Khan, Hisham Cholakal, Rao M. Anwer, Michael Felsberg, Tim Baldwin, Eric P. Xing, Fahad Shahbaz Khan
"Bigger the better" has been the predominant trend in recent Large Language Models (LLMs) development.
1 code implementation • 25 Feb 2024 • Sahal Shaji Mullappilly, Abhishek Singh Gehlot, Rao Muhammad Anwer, Fahad Shahbaz Khan, Hisham Cholakkal
We demonstrate the effectiveness of our SS-OWOD problem setting and approach for remote sensing object detection, proposing carefully curated splits and baseline performance evaluations.
1 code implementation • 20 Feb 2024 • Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal
In this paper, we introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic.
1 code implementation • 8 Feb 2024 • Senmao Li, Joost Van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang
However, these models struggle to effectively suppress the generation of undesired content, which is explicitly requested to be omitted from the generated image in the prompt.
no code implementations • 31 Dec 2023 • Syed Talal Wasim, Muzammal Naseer, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan
Our contributions include a novel spatio-temporal video grounding model, surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios.
1 code implementation • 15 Dec 2023 • Senmao Li, Taihang Hu, Fahad Shahbaz Khan, Linxuan Li, Shiqi Yang, Yaxing Wang, Ming-Ming Cheng, Jian Yang
This finding inspired us to omit the encoder at certain adjacent time-steps and reuse cyclically the encoder features in the previous time-steps for the decoder.
1 code implementation • 14 Dec 2023 • Sahal Shaji Mullappilly, Abdelrahman Shaker, Omkar Thawakar, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan
To this end, we propose a light-weight Arabic Mini-ClimateGPT that is built on an open-source LLM and is specifically fine-tuned on a conversational-style instruction tuning curated Arabic dataset Clima500-Instruct with over 500k instructions about climate change and sustainability.
1 code implementation • 27 Nov 2023 • Bin Xie, Jiale Cao, Jin Xie, Fahad Shahbaz Khan, Yanwei Pang
In this paper, we propose a simple encoder-decoder, named SED, for open-vocabulary semantic segmentation, which comprises a hierarchical encoder-based cost map generation and a gradual fusion decoder with category early rejection.
1 code implementation • 24 Nov 2023 • Kartik Kuckreja, Muhammad Sohail Danish, Muzammal Naseer, Abhijit Das, Salman Khan, Fahad Shahbaz Khan
Furthermore, the lack of domain-specific multimodal instruction following data as well as strong backbone models for RS make it hard for the models to align their behavior with user queries.
1 code implementation • 19 Nov 2023 • Rohit Bharadwaj, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
We present a novel approach to transform existing closed-set detectors into open-set detectors.
Ranked #1 on Novel Object Detection on LVIS v1.0 val
1 code implementation • NeurIPS 2023 • Muhammad Akhtar Munir, Salman Khan, Muhammad Haris Khan, Mohsen Ali, Fahad Shahbaz Khan
Third, we develop a logit mixing approach that acts as a regularizer with detection-specific losses and is also complementary to the uncertainty-guided logit modulation technique to further improve the calibration performance.
no code implementations • NeurIPS 2023 • Jameel Hassan, Hanan Gani, Noor Hussein, Muhammad Uzair Khattak, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan
The promising zero-shot generalization of vision-language models such as CLIP has led to their adoption using prompt learning for numerous downstream tasks.
no code implementations • 23 Oct 2023 • Adeel Yousaf, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah
Consistent improvements across multiple benchmarks and with various VLMs demonstrate the effectiveness of our proposed framework.
Ranked #2 on Video-Text Retrieval on Test-of-Time
1 code implementation • ICCV 2023 • Nian Liu, Kepan Nan, Wangbo Zhao, Yuanwei Liu, Xiwen Yao, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Junwei Han, Fahad Shahbaz Khan
We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively.
1 code implementation • 19 Sep 2023 • Mamona Awan, Muhammad Haris Khan, Sanoojan Baliah, Muhammad Ahmad Waseem, Salman Khan, Fahad Shahbaz Khan, Arif Mahmood
In the current work, we introduce a consistency-guided bottleneck in an image reconstruction-based pipeline that leverages landmark consistency, a measure of compatibility score with the pseudo-ground truth to generate adaptive heatmaps.
1 code implementation • 9 Sep 2023 • Chao Qin, Jiale Cao, Huazhu Fu, Rao Muhammad Anwer, Fahad Shahbaz Khan
Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of deep backbone features based on the self-attention operation.
1 code implementation • 30 Aug 2023 • Basit Alawode, Fayaz Ali Dharejo, Mehnaz Ummar, Yuhang Guo, Arif Mahmood, Naoufel Werghi, Fahad Shahbaz Khan, Jiri Matas, Sajid Javed
The method has resulted in a significant performance improvement, of up to 5. 0% AUC, of state-of-the-art (SOTA) visual trackers.
1 code implementation • 27 Jul 2023 • Haotong Qin, Ge-Peng Ji, Salman Khan, Deng-Ping Fan, Fahad Shahbaz Khan, Luc van Gool
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI.
1 code implementation • 25 Jul 2023 • Muhammad Awais, Muzammal Naseer, Salman Khan, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Fahad Shahbaz Khan
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
1 code implementation • 14 Jul 2023 • Asif Hanif, Muzammal Naseer, Salman Khan, Mubarak Shah, Fahad Shahbaz Khan
While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks.
2 code implementations • ICCV 2023 • Muhammad Uzair Khattak, Syed Talal Wasim, Muzammal Naseer, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan
To the best of our knowledge, this is the first regularization framework for prompt learning that avoids overfitting by jointly attending to pre-trained model features, the training trajectory during prompting, and the textual diversity.
Ranked #2 on Prompt Engineering on ImageNet V2
2 code implementations • ICCV 2023 • Syed Talal Wasim, Muhammad Uzair Khattak, Muzammal Naseer, Salman Khan, Mubarak Shah, Fahad Shahbaz Khan
Video transformer designs are based on self-attention that can model global context at a high computational cost.
Ranked #1 on Action Recognition on Diving-48
1 code implementation • 22 Jun 2023 • Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan
We present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation.
1 code implementation • 21 Jun 2023 • Nicolae-Catalin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, Mubarak Shah
We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level.
Ranked #13 on Anomaly Detection on CUHK Avenue
1 code implementation • 15 Jun 2023 • Shahina Kunhimon, Abdelrahman Shaker, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention.
1 code implementation • 13 Jun 2023 • Omkar Thawkar, Abdelrahman Shaker, Sahal Shaji Mullappilly, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Jorma Laaksonen, Fahad Shahbaz Khan
The latest breakthroughs in large vision-language models, such as Bard and GPT-4, have showcased extraordinary abilities in performing a wide range of tasks.
1 code implementation • 8 Jun 2023 • Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data.
Ranked #3 on Question Answering on NExT-QA (Open-ended VideoQA)
Video-based Generative Performance Benchmarking (Consistency) Video-based Generative Performance Benchmarking (Contextual Understanding) +5
1 code implementation • 6 Jun 2023 • Hefeng Wang, Jiale Cao, Rao Muhammad Anwer, Jin Xie, Fahad Shahbaz Khan, Yanwei Pang
Our DFormer outperforms the recent diffusion-based panoptic segmentation method Pix2Seq-D with a gain of 3. 6% on MS COCO val2017 set.
1 code implementation • 26 May 2023 • Xi Weng, Yunhao Ni, Tengwei Song, Jie Luo, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan, Lei Huang
In this work, we introduce Spectral Transformation (ST), a framework to modulate the spectrum of embedding and to seek for functions beyond whitening that can avoid dimensional collapse.
1 code implementation • 11 May 2023 • Dmitry Demidov, Muhammad Hamza Sharif, Aliakbar Abdurahimov, Hisham Cholakkal, Fahad Shahbaz Khan
Fine-grained visual classification (FGVC) is a challenging computer vision problem, where the task is to automatically recognise objects from subordinate categories.
1 code implementation • CVPR 2023 • Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan
Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules.
Ranked #1 on Co-Salient Object Detection on CoSal2015
1 code implementation • 13 Apr 2023 • Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan
Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-training on another CD dataset and then fine-tuning on the target benchmark.
1 code implementation • CVPR 2023 • Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan
Burst image processing is becoming increasingly popular in recent years.
1 code implementation • CVPR 2023 • Syed Talal Wasim, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah
Through this prompting scheme, we can achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and UCF101 while remaining competitive in the supervised setting.
1 code implementation • 4 Apr 2023 • Amandeep Kumar, Ankan Kumar Bhunia, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Jorma Laaksonen, Fahad Shahbaz Khan
In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues.
1 code implementation • 3 Apr 2023 • Omkar Thawakar, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Jorma Laaksonen, Mubarak Shah, Fahad Shahbaz Khan
Open-world formulation relaxes the close-world static-learning assumption as follows: (a) first, it distinguishes a set of known categories as well as labels an unknown object as `unknown' and then (b) it incrementally learns the class of an unknown as and when the corresponding semantic labels become available.
1 code implementation • ICCV 2023 • Amandeep Kumar, Ankan Kumar Bhunia, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan
We present a method to efficiently generate 3D-aware high-resolution images that are view-consistent across multiple target views.
1 code implementation • CVPR 2023 • Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming-Hsuan Yang
Unlike existing methods, the proposed alignment module not only aligns burst features but also exchanges feature information and maintains focused communication with the reference frame through the proposed reference-based feature enrichment mechanism, which facilitates handling complex motions.
1 code implementation • 28 Mar 2023 • Senmao Li, Joost Van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang
A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.
Ranked #7 on Text-based Image Editing on PIE-Bench
1 code implementation • CVPR 2023 • Senmao Li, Joost Van de Weijer, Yaxing Wang, Fahad Shahbaz Khan, Meiqin Liu, Jian Yang
In the second step, based on the well-trained multi-class 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware I2I translation system.
2 code implementations • ICCV 2023 • Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan
Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed.
1 code implementation • CVPR 2023 • Muhammad Akhtar Munir, Muhammad Haris Khan, Salman Khan, Fahad Shahbaz Khan
Since the original formulation of our loss depends on the counts of true positives and false positives in a minibatch, we develop a differentiable proxy of our loss that can be used during training with other application-specific loss functions.
1 code implementation • 21 Mar 2023 • Omkar Thawakar, Rao Muhammad Anwer, Jorma Laaksonen, Orly Reiner, Mubarak Shah, Fahad Shahbaz Khan
Accurate 3D mitochondria instance segmentation in electron microscopy (EM) is a challenging problem and serves as a prerequisite to empirically analyze their distributions and morphology.
no code implementations • ICCV 2023 • Salwa Al Khatib, Mohamed El Amine Boudjoghra, Jean Lahoud, Fahad Shahbaz Khan
Specifically, we provide the transformer block with spatial features to facilitate differentiation between similar object queries and incorporate semantic supervision to enhance prediction accuracy based on object class.
no code implementations • 30 Dec 2022 • Muzammal Naseer, Salman Khan, Fatih Porikli, Fahad Shahbaz Khan
Recently, different adversarial training defenses are proposed that not only maintain a high clean accuracy but also show significant robustness against popular and well studied adversarial attacks such as PGD.
2 code implementations • 8 Dec 2022 • Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks.
1 code implementation • CVPR 2023 • Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan
Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain.
no code implementations • 28 Nov 2022 • Nicolae-Catalin Ristea, Florinel-Alin Croitoru, Dana Dascalescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models.
Ranked #16 on Anomaly Detection on CUHK Avenue
1 code implementation • CVPR 2023 • Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Jorma Laaksonen, Mubarak Shah, Fahad Shahbaz Khan
In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution.
1 code implementation • 7 Oct 2022 • Xi Weng, Lei Huang, Lei Zhao, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan
A desirable objective in self-supervised learning (SSL) is to avoid feature collapse.
1 code implementation • 7 Oct 2022 • Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan, Rao Muhammad Anwer, Fahad Shahbaz Khan
Our PS-ARM achieves state-of-the-art performance on both datasets.
2 code implementations • CVPR 2023 • Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks.
Ranked #2 on Prompt Engineering on ImageNet-A
1 code implementation • 25 Sep 2022 • Neelu Madan, Nicolae-Catalin Ristea, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah
In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
Ranked #4 on Anomaly Detection on CUHK Avenue
no code implementations • 13 Sep 2022 • Dhanalaxmi Gaddam, Jean Lahoud, Fahad Shahbaz Khan, Rao Muhammad Anwer, Hisham Cholakkal
In this work, we propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework, which takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene at multiple levels to predict a set of object bounding-boxes along with their corresponding semantic labels.
no code implementations • 2 Sep 2022 • Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, Fahad Shahbaz Khan
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade.
1 code implementation • 14 Aug 2022 • Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, Christoph Mayer, Akshay Dudhane, Martin Danelljan, Hisham Cholakkal, Salman Khan, Luc van Gool, Fahad Shahbaz Khan
While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects.
no code implementations • 10 Aug 2022 • Xiaoheng Jiang, Xinyi Wu, Hisham Cholakkal, Rao Muhammad Anwer, Jiale Cao Mingliang Xu, Bing Zhou, Yanwei Pang, Fahad Shahbaz Khan
The SkipAgg module directly propagates features with small receptive fields to features with much larger receptive fields.
1 code implementation • 8 Aug 2022 • Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Ming-Hsuan Yang
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field.
2 code implementations • 25 Jul 2022 • Maryam Sultana, Muzammal Naseer, Muhammad Haris Khan, Salman Khan, Fahad Shahbaz Khan
Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains.
1 code implementation • 18 Jul 2022 • Hashmat Shadab Malik, Shahina K Kunhimon, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
Our training approach is based on a min-max scheme which reduces overfitting via an adversarial objective and thus optimizes for a more generalizable surrogate model.
no code implementations • 16 Jul 2022 • Antonio Barbalau, Radu Tudor Ionescu, Mariana-Iuliana Georgescu, Jacob Dueholm, Bharathkumar Ramachandra, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature.
Ranked #2 on Anomaly Detection on CUHK Avenue
1 code implementation • 7 Jul 2022 • Hanoona Rasheed, Muhammad Maaz, Muhammad Uzair Khattak, Salman Khan, Fahad Shahbaz Khan
Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and image-level supervision.
Ranked #1 on Open Vocabulary Object Detection on OpenImages-v4
1 code implementation • 5 Jul 2022 • Mamshad Nayeem Rizve, Navid Kardan, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah
In the open-world SSL problem, the objective is to recognize samples of known classes, and simultaneously detect and cluster samples belonging to novel classes present in unlabeled data.
Ranked #1 on Open-World Semi-Supervised Learning on CIFAR-10
7 code implementations • 21 Jun 2022 • Muhammad Maaz, Abdelrahman Shaker, Hisham Cholakkal, Salman Khan, Syed Waqas Zamir, Rao Muhammad Anwer, Fahad Shahbaz Khan
Our EdgeNeXt model with 1. 3M parameters achieves 71. 2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute gain of 2. 2% with 28% reduction in FLOPs.
Ranked #29 on Semantic Segmentation on PASCAL VOC 2012 test
2 code implementations • 11 May 2022 • Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
no code implementations • 22 Apr 2022 • Jyoti Kini, Fahad Shahbaz Khan, Salman Khan, Mubarak Shah
We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation.
1 code implementation • 19 Apr 2022 • Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao
In the former case, spatial details are preserved but the contextual information cannot be precisely encoded.
1 code implementation • 8 Apr 2022 • Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Andreea-Iuliana Miron, Olivian Savencu, Nicolae-Catalin Ristea, Nicolae Verga, Fahad Shahbaz Khan
Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input tensors, where the kernel (receptive field) size controls the reduction rate of the spatial attention, and the number of convolutional filters controls the reduction rate of the channel attention, respectively.
Ranked #1 on Image Super-Resolution on IXI
1 code implementation • CVPR 2022 • Jiale Cao, Yanwei Pang, Rao Muhammad Anwer, Hisham Cholakkal, Jin Xie, Mubarak Shah, Fahad Shahbaz Khan
We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture.
2 code implementations • CVPR 2022 • K J Joseph, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Vineeth N Balasubramanian
Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks.
1 code implementation • 24 Mar 2022 • Omkar Thawakar, Sanath Narayan, Jiale Cao, Hisham Cholakkal, Rao Muhammad Anwer, Muhammad Haris Khan, Salman Khan, Michael Felsberg, Fahad Shahbaz Khan
When using the ResNet50 backbone, our MS-STS achieves a mask AP of 50. 1 %, outperforming the best reported results in literature by 2. 7 % and by 4. 8 % at higher overlap threshold of AP_75, while being comparable in model size and speed on Youtube-VIS 2019 val.
1 code implementation • 17 Mar 2022 • Nicolae-Catalin Ristea, Radu Tudor Ionescu, Fahad Shahbaz Khan
Following the successful application of vision transformers in multiple computer vision tasks, these models have drawn the attention of the signal processing community.
Ranked #1 on Time Series Analysis on Speech Commands
1 code implementation • 24 Jan 2022 • Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.
1 code implementation • CVPR 2022 • Anirudh Thatipelli, Sanath Narayan, Salman Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Bernard Ghanem
Experiments are performed on four few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101.
Ranked #1 on Few Shot Action Recognition on UCF101 (using extra training data)
no code implementations • 6 Dec 2021 • Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Jorma Laaksonen, Michael Felsberg
Creative sketch image generation is a challenging vision problem, where the task is to generate diverse, yet realistic creative sketches possessing the unseen composition of the visual-world objects.
no code implementations • 6 Dec 2021 • Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, Muhammad Haris Khan, Michael Felsberg, Jiri Matas
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems.
1 code implementation • CVPR 2022 • Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Michael Ryoo
To the best of our knowledge, the proposed approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT).
Ranked #55 on Action Recognition on UCF101
2 code implementations • CVPR 2022 • Akshita Gupta, Sanath Narayan, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah
In the case of incremental object detection, OW-DETR outperforms the state-of-the-art for all settings on PASCAL VOC.
1 code implementation • 22 Nov 2021 • Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Ming-Hsuan Yang
This has been a long-standing question in computer vision.
11 code implementations • CVPR 2022 • Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Ranked #1 on Grayscale Image Denoising on Urban100 sigma15
4 code implementations • CVPR 2022 • Nicolae-Catalin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah
Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field.
Ranked #1 on Anomaly Detection on CUHK Avenue (TBDC metric)
1 code implementation • CVPR 2022 • Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
This is a closed-set scenario that fails to test the capability of systems at detecting new anomaly types.
Ranked #5 on Anomaly Detection on CUHK Avenue (using extra training data)
1 code implementation • 12 Oct 2021 • Nicolae-Catalin Ristea, Andreea-Iuliana Miron, Olivian Savencu, Mariana-Iuliana Georgescu, Nicolae Verga, Fahad Shahbaz Khan, Radu Tudor Ionescu
Our neural model can be trained on unpaired images, due to the integration of a multi-level cycle-consistency loss.
1 code implementation • 7 Oct 2021 • Joakim Johnander, Johan Edstedt, Michael Felsberg, Fahad Shahbaz Khan, Martin Danelljan
Given the support set, our dense GP learns the mapping from local deep image features to mask values, capable of capturing complex appearance distributions.
Ranked #1 on Few-Shot Semantic Segmentation on COCO-20i (10-shot)
1 code implementation • CVPR 2022 • Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming-Hsuan Yang
Our central idea is to create a set of pseudo-burst features that combine complementary information from all the input burst frames to seamlessly exchange information.
Ranked #2 on Burst Image Super-Resolution on BurstSR
1 code implementation • ICCV 2021 • Sanath Narayan, Akshita Gupta, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Mubarak Shah
We note that the best existing multi-label ZSL method takes a shared approach towards attending to region features with a common set of attention maps for all the classes.
Ranked #2 on Multi-label zero-shot learning on Open Images V4
no code implementations • 15 Jul 2021 • Puneet Mangla, Shivam Chandhok, Vineeth N Balasubramanian, Fahad Shahbaz Khan
Recent progress towards designing models that can generalize to unseen domains (i. e domain generalization) or unseen classes (i. e zero-shot learning) has embarked interest towards building models that can tackle both domain-shift and semantic shift simultaneously (i. e zero-shot domain generalization).
no code implementations • 12 Jul 2021 • Shivam Chandhok, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Vineeth N Balasubramanian, Fahad Shahbaz Khan, Ling Shao
The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately address the issues of semantic shift and domain shift, respectively.
3 code implementations • ICLR 2022 • Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Fahad Shahbaz Khan, Fatih Porikli
(ii) Token Refinement: We then propose to refine the tokens to further enhance the discriminative capacity at each block of ViT.
1 code implementation • NeurIPS 2021 • Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang
We show and analyze the following intriguing properties of ViT: (a) Transformers are highly robust to severe occlusions, perturbations and domain shifts, e. g., retain as high as 60% top-1 accuracy on ImageNet even after randomly occluding 80% of the image content.
1 code implementation • 28 Apr 2021 • Yaxing Wang, Abel Gonzalez-Garcia, Chenshen Wu, Luis Herranz, Fahad Shahbaz Khan, Shangling Jui, Joost Van de Weijer
Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.
no code implementations • 26 Apr 2021 • Mohamed Afham, Salman Khan, Muhammad Haris Khan, Muzammal Naseer, Fahad Shahbaz Khan
Human learning benefits from multi-modal inputs that often appear as rich semantics (e. g., description of an object's attributes while learning about it).
Ranked #1 on Few-Shot Image Classification on Oxford 102 Flower (using extra training data)
1 code implementation • ICCV 2021 • Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Mubarak Shah
We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local writing style patterns.
no code implementations • 30 Mar 2021 • Joakim Johnander, Johan Edstedt, Martin Danelljan, Michael Felsberg, Fahad Shahbaz Khan
Through the expressivity of the GP, our approach is capable of modeling complex appearance distributions in the deep feature space.
3 code implementations • ICCV 2021 • Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Fatih Porikli
To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains.
1 code implementation • ICCV 2021 • Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan
The CE loss encourages features of a class to have a higher projection score on the true class-vector compared to the negative classes.
2 code implementations • CVPR 2021 • K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian
Humans have a natural instinct to identify unknown object instances in their environments.
1 code implementation • CVPR 2021 • Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah
Equivariance or invariance has been employed standalone in the previous works; however, to the best of our knowledge, they have not been used jointly.
8 code implementations • CVPR 2021 • Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Ranked #3 on Spectral Reconstruction on ARAD-1K
1 code implementation • 27 Jan 2021 • Akshita Gupta, Sanath Narayan, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Joost Van de Weijer
Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge.
Ranked #8 on Multi-label zero-shot learning on NUS-WIDE
no code implementations • 4 Jan 2021 • Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah
Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems.
no code implementations • 4 Jan 2021 • Aditya Arora, Muhammad Haris, Syed Waqas Zamir, Munawar Hayat, Fahad Shahbaz Khan, Ling Shao, Ming-Hsuan Yang
These contexts can be crucial towards inferring several image enhancement tasks, e. g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent.
1 code implementation • ICCV 2021 • Sanath Narayan, Hisham Cholakkal, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao
The proposed formulation comprises a discriminative and a denoising loss term for enhancing temporal action localization.
Ranked #3 on Weakly Supervised Action Localization on THUMOS’14
1 code implementation • CVPR 2021 • Wencheng Han, Xingping Dong, Fahad Shahbaz Khan, Ling Shao, Jianbing Shen
We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on large-scale data.
Ranked #22 on Visual Object Tracking on TrackingNet
1 code implementation • CVPR 2021 • Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, Mubarak Shah
To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.
Ranked #2 on Anomaly Detection on UCSD Peds2
Abnormal Event Detection In Video Anomaly Detection In Surveillance Videos +4
2 code implementations • 19 Oct 2020 • Nasir Hayat, Munawar Hayat, Shafin Rahman, Salman Khan, Syed Waqas Zamir, Fahad Shahbaz Khan
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference.
Ranked #1 on Zero-Shot Object Detection on ImageNet Detection
no code implementations • 19 Oct 2020 • Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Mubarak Shah
This demonstrates their ability to acquire transferable knowledge, a capability that is central to human learning.
2 code implementations • 1 Oct 2020 • Jiale Cao, Yanwei Pang, Jin Xie, Fahad Shahbaz Khan, Ling Shao
In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection, which provides more robust features for illumination variance.
no code implementations • 25 Sep 2020 • Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, WangMeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Tangxin Xie, Liang Cao, Yan Zou, Yi Shen, Jialiang Zhang, Yu Jia, Kaihua Cheng, Chenhuan Wu, Yue Lin, Cen Liu, Yunbo Peng, Xueyi Zou, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Tongtong Zhao, Shanshan Zhao, Yoseob Han, Byung-Hoon Kim, JaeHyun Baek, HaoNing Wu, Dejia Xu, Bo Zhou, Wei Guan, Xiaobo Li, Chen Ye, Hao Li, Yukai Shi, Zhijing Yang, Xiaojun Yang, Haoyu Zhong, Xin Li, Xin Jin, Yaojun Wu, Yingxue Pang, Sen Liu, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani, Wan-Chi Siu, Yuanbo Zhou, Rao Muhammad Umer, Christian Micheloni, Xiaofeng Cong, Rajat Gupta, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Feras Almasri, Thomas Vandamme, Olivier Debeir
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
2 code implementations • 27 Aug 2020 • Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, Mubarak Shah
Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.
Abnormal Event Detection In Video Anomaly Detection In Surveillance Videos +2
1 code implementation • 25 Aug 2020 • Saeed Anwar, Muhammad Tahir, Chongyi Li, Ajmal Mian, Fahad Shahbaz Khan, Abdul Wahab Muzaffar
Image colorization is the process of estimating RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality.
1 code implementation • 29 Jul 2020 • Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Fatih Porikli
In contrast to existing adversarial training methods that only use class-boundary information (e. g., using a cross-entropy loss), we propose to exploit additional information from the feature space to craft stronger adversaries that are in turn used to learn a robust model.
1 code implementation • ECCV 2020 • Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao
In terms of real-time capabilities, SipMask outperforms YOLACT with an absolute gain of 3. 0% (mask AP) under similar settings, while operating at comparable speed on a Titan Xp.
Ranked #12 on Real-time Instance Segmentation on MSCOCO
1 code implementation • 17 Jun 2020 • Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Mubarak Shah
Our experiments show that, even in the first stage, self-supervision can outperform current state-of-the-art methods, with further gains achieved by our second stage distillation process.
Ranked #12 on Few-Shot Image Classification on FC100 5-way (5-shot)
2 code implementations • CVPR 2020 • Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Fatih Porikli
Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e. g., for classification, segmentation and object detection.
1 code implementation • CVPR 2020 • Tiancai Wang, Tong Yang, Martin Danelljan, Fahad Shahbaz Khan, Xiangyu Zhang, Jian Sun
Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them.
1 code implementation • CVPR 2020 • Yaxing Wang, Salman Khan, Abel Gonzalez-Garcia, Joost Van de Weijer, Fahad Shahbaz Khan
In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training.
1 code implementation • CVPR 2020 • Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Mubarak Shah
In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks.
2 code implementations • 17 Mar 2020 • K J Joseph, Jathushan Rajasegaran, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian
In a real-world setting, object instances from new classes can be continuously encountered by object detectors.
8 code implementations • CVPR 2020 • Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao
This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline.
Ranked #10 on Image Denoising on DND (using extra training data)
1 code implementation • ECCV 2020 • Sanath Narayan, Akshita Gupta, Fahad Shahbaz Khan, Cees G. M. Snoek, Ling Shao
We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classification.
Ranked #2 on Generalized Zero-Shot Learning on CUB-200-2011
no code implementations • 16 Mar 2020 • Shafin Rahman, Salman Khan, Nick Barnes, Fahad Shahbaz Khan
Any-shot detection offers unique challenges compared to conventional novel object detection such as, a high imbalance between unseen, few-shot and seen object classes, susceptibility to forget base-training while learning novel classes and distinguishing novel classes from the background.
12 code implementations • ECCV 2020 • Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.
Ranked #5 on Spectral Reconstruction on ARAD-1K
2 code implementations • CVPR 2020 • Andreas Robinson, Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation.
no code implementations • 25 Jan 2020 • Jin Xie, Yanwei Pang, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Ling Shao
On the heavy occluded (\textbf{HO}) set of CityPerosns test set, our PSC-Net obtains an absolute gain of 4. 0\% in terms of log-average miss rate over the state-of-the-art with same backbone, input scale and without using additional VBB supervision.
no code implementations • 14 Dec 2019 • Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Shahbaz Khan, Ling Shao
The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other.
Ranked #16 on Fine-Grained Image Classification on Stanford Dogs
1 code implementation • 13 Dec 2019 • Hisham Cholakkal, Guolei Sun, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Luc van Gool
Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones.
Image Classification Image-level Supervised Instance Segmentation +3
2 code implementations • CVPR 2020 • Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, Joost Van de Weijer
We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.
1 code implementation • NeurIPS 2019 • Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, Ling Shao
In order to maintain an equilibrium between previous and newly acquired knowledge, we propose a simple controller to dynamically balance the model plasticity.
Ranked #7 on Continual Learning on F-CelebA (10 tasks)
no code implementations • ICCV 2019 • Tiancai Wang, Rao Muhammad Anwer, Muhammad Haris Khan, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao, Jorma Laaksonen
Our approach outperforms the state-of-the-art on all datasets.
1 code implementation • ICCV 2019 • Yanwei Pang, Jin Xie, Muhammad Haris Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Ling Shao
Our approach obtains an absolute gain of 9. 5% in log-average miss rate, compared to the best reported results on the heavily occluded (HO) pedestrian set of CityPersons test set.
1 code implementation • CVPR 2020 • Muhammad Haris Khan, John McDonagh, Salman Khan, Muhammad Shahabuddin, Aditya Arora, Fahad Shahbaz Khan, Ling Shao, Georgios Tzimiropoulos
Several studies show that animal needs are often expressed through their faces.
1 code implementation • 30 Aug 2019 • Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia, Joost Van de Weijer, Fahad Shahbaz Khan
Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities.
Ranked #10 on Rgb-T Tracking on RGBT210
1 code implementation • ICCV 2019 • Sanath Narayan, Hisham Cholakkal, Fahad Shahbaz Khan, Ling Shao
Our joint formulation has three terms: a classification term to ensure the separability of learned action features, an adapted multi-label center loss term to enhance the action feature discriminability and a counting loss term to delineate adjacent action sequences, leading to improved localization.
Ranked #1 on Action Classification on THUMOS'14
Action Classification Weakly Supervised Action Localization +2
1 code implementation • ICCV 2019 • Lichao Zhang, Abel Gonzalez-Garcia, Joost Van de Weijer, Martin Danelljan, Fahad Shahbaz Khan
In general, this template is linearly combined with the accumulated template from the previous frame, resulting in an exponential decay of information over time.
no code implementations • 24 Jul 2019 • Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi
This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools.
1 code implementation • 3 Jun 2019 • Jathushan Rajasegaran, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Ming-Hsuan Yang
In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage.
3 code implementations • 30 May 2019 • Syed Waqas Zamir, Aditya Arora, Akshita Gupta, Salman Khan, Guolei Sun, Fahad Shahbaz Khan, Fan Zhu, Ling Shao, Gui-Song Xia, Xiang Bai
Compared to existing small-scale aerial image based instance segmentation datasets, iSAID contains 15$\times$ the number of object categories and 5$\times$ the number of instances.
Ranked #1 on Object Detection on iSAID
2 code implementations • NeurIPS 2019 • Muzammal Naseer, Salman H. Khan, Harris Khan, Fahad Shahbaz Khan, Fatih Porikli
To this end, we propose a framework capable of launching highly transferable attacks that crafts adversarial patterns to mislead networks trained on wholly different domains.
no code implementations • 18 Apr 2019 • Andreas Robinson, Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
We propose a novel approach, based on a dedicated target appearance model that is exclusively learned online to discriminate between the target and background image regions.
1 code implementation • CVPR 2019 • Devraj Mandal, Sanath Narayan, Saikumar Dwivedi, Vikram Gupta, Shuaib Ahmed, Fahad Shahbaz Khan, Ling Shao
We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category.
Action Recognition In Videos Out-of-Distribution Detection +2
no code implementations • 11 Apr 2019 • Syed Waqas Zamir, Aditya Arora, Salman Khan, Fahad Shahbaz Khan, Ling Shao
In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio (SNR).
2 code implementations • CVPR 2019 • Hisham Cholakkal, Guolei Sun, Fahad Shahbaz Khan, Ling Shao
Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17. 8% in terms of average best overlap, on the PASCAL VOC 2012 dataset.
Ranked #1 on Object Counting on COCO count-test
1 code implementation • CVPR 2019 • Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, Ling Shao
Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training.
Ranked #14 on Anomaly Detection on ShanghaiTech
1 code implementation • CVPR 2019 • Joakim Johnander, Martin Danelljan, Emil Brissman, Fahad Shahbaz Khan, Michael Felsberg
One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance.
3 code implementations • CVPR 2019 • Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.
Ranked #7 on Object Tracking on FE108
1 code implementation • 5 Nov 2018 • Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan
In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work.
Ranked #7 on Depth Completion on KITTI Depth Completion
no code implementations • 4 Jun 2018 • Lichao Zhang, Abel Gonzalez-Garcia, Joost Van de Weijer, Martin Danelljan, Fahad Shahbaz Khan
These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking.
1 code implementation • 30 May 2018 • Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan
To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task.
no code implementations • ECCV 2018 • Goutam Bhat, Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
In the field of generic object tracking numerous attempts have been made to exploit deep features.
1 code implementation • CVPR 2018 • Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Per-Erik Forssén, Michael Felsberg
Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes.
no code implementations • 9 Jun 2017 • Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
Generally, DCF based trackers learn a rigid appearance model of the target.
no code implementations • 5 Jun 2017 • Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost Van de Weijer, Matthieu Molinier, Jorma Laaksonen
To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification.
Ranked #12 on Aerial Scene Classification on AID (20% as trainset)
1 code implementation • 9 May 2017 • Felix Järemo Lawin, Martin Danelljan, Patrik Tosteberg, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results.
Ranked #15 on Semantic Segmentation on Semantic3D
no code implementations • 20 Dec 2016 • Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking.
no code implementations • 14 Dec 2016 • Fahad Shahbaz Khan, Joost Van de Weijer, Rao Muhammad Anwer, Andrew D. Bagdanov, Michael Felsberg, Jorma Laaksonen
Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding.
5 code implementations • CVPR 2017 • Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65. 0% AUC on OTB-2015.
Ranked #13 on Visual Object Tracking on VOT2017/18
no code implementations • CVPR 2016 • Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks.
no code implementations • 20 Sep 2016 • Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
Compared to the standard exhaustive scale search, our approach achieves a gain of 2. 5% in average overlap precision on the OTB dataset.
no code implementations • ICCV 2015 • Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood.
1 code implementation • 12 Aug 2016 • Martin Danelljan, Andreas Robinson, Fahad Shahbaz Khan, Michael Felsberg
We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.
no code implementations • CVPR 2016 • Martin Danelljan, Giulia Meneghetti, Fahad Shahbaz Khan, Michael Felsberg
On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline.
no code implementations • CVPR 2014 • Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg, Joost Van de Weijer
This paper investigates the contribution of color in a tracking-by-detection framework.
no code implementations • CVPR 2013 • Rahat Khan, Joost Van de Weijer, Fahad Shahbaz Khan, Damien Muselet, Christophe Ducottet, Cecile Barat
This results in a drop of discriminative power of the color description.