1 code implementation • 24 Mar 2024 • Siddharth Tourani, Ahmed Alwheibi, Arif Mahmood, Muhammad Haris Khan
Second, motivated by the ZeroShot performance, we develop a ULD algorithm based on diffusion features using self-training and clustering which also outperforms prior methods by notable margins.
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.
no code implementations • 17 Sep 2023 • Arif Mahmood, Abdul Basit, M. Akhtar Munir, Mohsen Ali
By combining these components, our approach achieves exceptional results on a newly proposed dataset.
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.
no code implementations • 14 May 2023 • Hira Yaseen, Arif Mahmood
Spectral Embedding (SE) has often been used to map data points from non-linear manifolds to linear subspaces for the purpose of classification and clustering.
no code implementations • 3 May 2023 • Sajid Javed, Arif Mahmood, Talha Qaiser, Naoufel Werghi, Nasir Rajpoot
There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs.
1 code implementation • 10 Mar 2023 • Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan, Muhammad Zaigham Zaheer, Karthik Nandakumar, Muhammad Haroon Yousaf, Arif Mahmood
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text.
no code implementations • 21 Feb 2023 • Jhony H. Giraldo, Sajid Javed, Arif Mahmood, Fragkiskos D. Malliaros, Thierry Bouwmans
Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.
1 code implementation • 21 Oct 2022 • Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood
A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a discriminative multi-scale facial representations for face recognition and verification.
no code implementations • 25 Aug 2022 • Maria Marrium, Arif Mahmood
Graph Neural Network (GNNs) based methods have recently become a popular tool to deal with graph data because of their ability to incorporate structural information.
1 code implementation • 13 Jul 2022 • Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou, Thierry Bouwmans
In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples.
no code implementations • 6 Jul 2022 • Shahzad Ali, Arif Mahmood, Soon Ki Jung
We developed a model that is similar in spirit to the well-established encoder-decoder and residual convolution neural networks.
no code implementations • 25 Mar 2022 • Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions.
no code implementations • 25 Mar 2022 • Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data.
1 code implementation • 8 Mar 2022 • Mehwish Ghafoor, Arif Mahmood
Our experiments demonstrate the effectiveness of the proposed framework for handling the missing joints as well as quantification of the occlusion handling capability of the deep neural networks.
no code implementations • CVPR 2022 • Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Mattia Segu, Fisher Yu, Seung-Ik Lee
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings.
no code implementations • 30 Apr 2021 • Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, Arif Mahmood, Seung-Ik Lee
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels.
no code implementations • 3 Jan 2021 • Bilal Yousaf, Muhammad Usama, Waqas Sultani, Arif Mahmood, Junaid Qadir
The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors and fusing the frequency and spatial domain streams has also improved generalization of the detector.
no code implementations • ECCV 2020 • Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
The proposed method obtains83. 03% and 89. 67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.
no code implementations • 18 Nov 2020 • Nazar Khan, Arbish Akram, Arif Mahmood, Sania Ashraf, Kashif Murtaza
Compared to facial expression recognition, expression synthesis requires a very high-dimensional mapping.
Facial Expression Recognition Facial Expression Recognition (FER) +1
no code implementations • 27 Aug 2020 • Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin, Seung-Ik Lee
Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community.
no code implementations • 19 May 2020 • Abdul Basit, Muhammad Akhtar Munir, Mohsen Ali, Arif Mahmood
Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm).
no code implementations • 28 Apr 2020 • Muhammad Saad Saeed, Shah Nawaz, Pietro Morerio, Arif Mahmood, Ignazio Gallo, Muhammad Haroon Yousaf, Alessio Del Bue
Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition.
no code implementations • 18 Sep 2019 • Shah Nawaz, Muhammad Kamran Janjua, Ignazio Gallo, Arif Mahmood, Alessandro Calefati
We quantitatively and qualitatively evaluate the proposed approach on VoxCeleb, a benchmarks audio-visual dataset on a multitude of tasks including cross-modal verification, cross-modal matching, and cross-modal retrieval.
no code implementations • 3 Sep 2019 • Shah Nawaz, Muhammad Kamran Janjua, Ignazio Gallo, Arif Mahmood, Alessandro Calefati, Faisal Shafait
Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space.
1 code implementation • 22 Apr 2019 • Javed Iqbal, Muhammad Akhtar Munir, Arif Mahmood, Afsheen Rafaqat Ali, Mohsen Ali
The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors.
no code implementations • 6 Dec 2018 • Mustansar Fiaz, Arif Mahmood, Sajid Javed, Soon Ki Jung
In order to overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms.
no code implementations • 5 Nov 2018 • Maryam Sultana, Arif Mahmood, Sajid Javed, Soon Ki Jung
To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy.
no code implementations • 21 May 2018 • Maryam Sultana, Arif Mahmood, Sajid Javed, Soon Ki Jung
Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations.
no code implementations • 9 Feb 2018 • Mustansar Fiaz, Arif Mahmood, Soon Ki Jung
In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise.
no code implementations • 29 Jan 2018 • Mustansar Fiaz, Sajid Javed, Arif Mahmood, Soon Ki Jung
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades.
no code implementations • 24 Sep 2014 • Hossein Rahmani, Arif Mahmood, Du Huynh, Ajmal Mian
We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations.
no code implementations • 17 Aug 2014 • Hossein Rahmani, Arif Mahmood, Du. Q. Huynh, Ajmal Mian
In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations.
no code implementations • 17 Aug 2014 • Hossein Rahmani, Arif Mahmood, Du Huynh, Ajmal Mian
We use the Histogram of Oriented Gradient (HOG3D) feature to encode the information in each cell.
no code implementations • 14 Jul 2014 • Arif Mahmood, Ajmal Mian, Robyn Owens
To this end we propose an Efficient Group Size (EGS) algorithm which minimizes the number of similarity computations for a particular search image.
no code implementations • CVPR 2014 • Arif Mahmood, Ajmal Mian, Robyn Owens
We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion.
no code implementations • 22 May 2014 • Arif Mahmood, Ajmal S. Mian
We propose a Classification Via Clustering (CVC) algorithm which enables existing clustering methods to be efficiently employed in classification problems.