1 code implementation • ECCV 2020 • Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, Tinne Tuytelaars
Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.
no code implementations • 17 Apr 2024 • Linfang Zheng, Tze Ho Elden Tse, Chen Wang, Yinghan Sun, Hua Chen, Ales Leonardis, Wei zhang
Object pose refinement is essential for robust object pose estimation.
no code implementations • 6 Oct 2023 • Jiali Zheng, Youngkyoon Jang, Athanasios Papaioannou, Christos Kampouris, Rolandos Alexandros Potamias, Foivos Paraperas Papantoniou, Efstathios Galanakis, Ales Leonardis, Stefanos Zafeiriou
This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-captured human head dataset designed to support view synthesis academic challenges for human heads.
1 code implementation • 2 Oct 2023 • Wei-Hong Li, Steven McDonagh, Ales Leonardis, Hakan Bilen
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.
1 code implementation • 13 Jul 2023 • Alexander Krull, Hector Basevi, Benjamin Salmon, Andre Zeug, Franziska Müller, Samuel Tonks, Leela Muppala, Ales Leonardis
This new perspective allows us to make three contributions: We present a new strategy for self-supervised denoising, We present a new method for sampling from the posterior of possible solutions by iteratively sampling and adding small numbers of photons to the image.
1 code implementation • CVPR 2023 • Linfang Zheng, Chen Wang, Yinghan Sun, Esha Dasgupta, Hua Chen, Ales Leonardis, Wei zhang, Hyung Jin Chang
In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation.
1 code implementation • CVPR 2023 • HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch, Yitong Li, Yannick Verdie, Jifei Song, Yiren Zhou, Anil Armagan, Slobodan Ilic, Ales Leonardis, Nassir Navab, Benjamin Busam
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data.
1 code implementation • ICCV 2023 • Francesca Babiloni, Matteo Maggioni, Thomas Tanay, Jiankang Deng, Ales Leonardis, Stefanos Zafeiriou
The success of deep learning models on structured data has generated significant interest in extending their application to non-Euclidean domains.
no code implementations • 9 Dec 2022 • Wei Chen, Xi Jia, Zhongqun Zhang, Hyung Jin Chang, Linlin Shen, Jinming Duan, Ales Leonardis
The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task.
no code implementations • 16 Nov 2022 • Nanqing Dong, Linus Ericsson, Yongxin Yang, Ales Leonardis, Steven McDonagh
In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance.
no code implementations • 9 Nov 2022 • William Thong, Jose Costa Pereira, Sarah Parisot, Ales Leonardis, Steven McDonagh
This restricts the diversity and number of image pairs that the model is exposed to during training.
no code implementations • 5 Aug 2022 • Xue Hu, Xinghui Li, Benjamin Busam, Yiren Zhou, Ales Leonardis, Shanxin Yuan
Specifically, we focus on human appearance and learn implicit pose, shape and garment representations of dressed humans from RGB images.
no code implementations • 1 Aug 2022 • Tze Ho Elden Tse, Zhongqun Zhang, Kwang In Kim, Ales Leonardis, Feng Zheng, Hyung Jin Chang
In this paper, we propose a novel semi-supervised framework that allows us to learn contact from monocular images.
no code implementations • 9 May 2022 • HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch, Yitong Li, Yannick Verdie, Jifei Song, Yiren Zhou, Anil Armagan, Slobodan Ilic, Ales Leonardis, Benjamin Busam
Depth estimation is a core task in 3D computer vision.
no code implementations • CVPR 2022 • Tze Ho Elden Tse, Kwang In Kim, Ales Leonardis, Hyung Jin Chang
Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality.
Ranked #6 on hand-object pose on DexYCB
no code implementations • 28 Mar 2022 • Richard Shaw, Sibi Catley-Chandar, Ales Leonardis, Eduardo Perez-Pellitero
Our proposed approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the HdM HDR dataset, respectively.
no code implementations • 23 Mar 2022 • Michal Nazarczuk, Sibi Catley-Chandar, Ales Leonardis, Eduardo Pérez Pellitero
Recent High Dynamic Range (HDR) techniques extend the capabilities of current cameras where scenes with a wide range of illumination can not be accurately captured with a single low-dynamic-range (LDR) image.
no code implementations • 7 Jan 2022 • Nora Horanyi, Kedi Xia, Kwang Moo Yi, Abhishake Kumar Bojja, Ales Leonardis, Hyung Jin Chang
We propose a novel optimization framework that crops a given image based on user description and aesthetics.
no code implementations • 7 Dec 2021 • HyunJun Jung, Nikolas Brasch, Ales Leonardis, Nassir Navab, Benjamin Busam
Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price.
no code implementations • 22 Oct 2021 • Song Yan, Jinyu Yang, Ales Leonardis, Joni-Kristian Kamarainen
There are two potential reasons for the heuristics: 1) the lack of large RGBD tracking datasets to train deep RGBD trackers and 2) the long-term evaluation protocol of VOT RGBD that benefits from heuristics such as depth-based occlusion detection.
no code implementations • 29 Sep 2021 • Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh
In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.
1 code implementation • 3 Aug 2021 • Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan
The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.
no code implementations • 18 Jun 2021 • Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh
We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.
no code implementations • 25 May 2021 • Xi Jia, Alexander Thorley, Wei Chen, Huaqi Qiu, Linlin Shen, Iain B Styles, Hyung Jin Chang, Ales Leonardis, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Jinming Duan
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
no code implementations • 7 May 2021 • Jinjin Gu, Haoming Cai, Chao Dong, Jimmy S. Ren, Yu Qiao, Shuhang Gu, Radu Timofte, Manri Cheon, SungJun Yoon, Byungyeon Kang, Junwoo Lee, Qing Zhang, Haiyang Guo, Yi Bin, Yuqing Hou, Hengliang Luo, Jingyu Guo, ZiRui Wang, Hai Wang, Wenming Yang, Qingyan Bai, Shuwei Shi, Weihao Xia, Mingdeng Cao, Jiahao Wang, Yifan Chen, Yujiu Yang, Yang Li, Tao Zhang, Longtao Feng, Yiting Liao, Junlin Li, William Thong, Jose Costa Pereira, Ales Leonardis, Steven McDonagh, Kele Xu, Lehan Yang, Hengxing Cai, Pengfei Sun, Seyed Mehdi Ayyoubzadeh, Ali Royat, Sid Ahmed Fezza, Dounia Hammou, Wassim Hamidouche, Sewoong Ahn, Gwangjin Yoon, Koki Tsubota, Hiroaki Akutsu, Kiyoharu Aizawa
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
2 code implementations • 11 Apr 2021 • Qingyong Hu, Bo Yang, Guangchi Fang, Yulan Guo, Ales Leonardis, Niki Trigoni, Andrew Markham
Labelling point clouds fully is highly time-consuming and costly.
2 code implementations • CVPR 2021 • Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Linlin Shen, Ales Leonardis
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image.
Ranked #7 on 6D Pose Estimation using RGBD on REAL275
1 code implementation • ICCV 2021 • Song Yan, Jinyu Yang, Jani Kapyla, Feng Zheng, Ales Leonardis, Joni-Kristian Kamarainen
This can be explained by the fact that there are no sufficiently large RGBD datasets to 1) train "deep depth trackers" and to 2) challenge RGB trackers with sequences for which the depth cue is essential.
no code implementations • 10 Oct 2020 • Thomas Tanay, Aivar Sootla, Matteo Maggioni, Puneet K. Dokania, Philip Torr, Ales Leonardis, Gregory Slabaugh
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution.
1 code implementation • ECCV 2020 • Carlo Biffi, Steven McDonagh, Philip Torr, Ales Leonardis, Sarah Parisot
Object detection has witnessed significant progress by relying on large, manually annotated datasets.
1 code implementation • 14 Jul 2020 • Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis, Wengang Zhou, Qi Tian
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
no code implementations • 6 May 2020 • Shanxin Yuan, Radu Timofte, Ales Leonardis, Gregory Slabaugh, Xiaotong Luo, Jiangtao Zhang, Yanyun Qu, Ming Hong, Yuan Xie, Cuihua Li, Dejia Xu, Yihao Chu, Qingyan Sun, Shuai Liu, Ziyao Zong, Nan Nan, Chenghua Li, Sangmin Kim, Hyungjoon Nam, Jisu Kim, Jechang Jeong, Manri Cheon, Sung-Jun Yoon, Byungyeon Kang, Junwoo Lee, Bolun Zheng, Xiaohong Liu, Linhui Dai, Jun Chen, Xi Cheng, Zhen-Yong Fu, Jian Yang, Chul Lee, An Gia Vien, Hyunkook Park, Sabari Nathan, M. Parisa Beham, S Mohamed Mansoor Roomi, Florian Lemarchand, Maxime Pelcat, Erwan Nogues, Densen Puthussery, Hrishikesh P. S, Jiji C. V, Ashish Sinha, Xuan Zhao
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
1 code implementation • CVPR 2020 • Bolun Zheng, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis
Image demoireing is a multi-faceted image restoration task involving both texture and color restoration.
Ranked #2 on Image Enhancement on TIP 2018 (using extra training data)
1 code implementation • CVPR 2020 • Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.
1 code implementation • CVPR 2020 • Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Ales Leonardis
Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation.
1 code implementation • CVPR 2020 • Daniel Hernandez-Juarez, Sarah Parisot, Benjamin Busam, Ales Leonardis, Gregory Slabaugh, Steven McDonagh
Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.
no code implementations • 8 Nov 2019 • Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis, Bolun Zheng, Xin Ye, Xiang Tian, Yaowu Chen, Xi Cheng, Zhen-Yong Fu, Jian Yang, Ming Hong, Wenying Lin, Wenjin Yang, Yanyun Qu, Hong-Kyu Shin, Joon-Yeon Kim, Sung-Jea Ko, Hang Dong, Yu Guo, Jie Wang, Xuan Ding, Zongyan Han, Sourya Dipta Das, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
no code implementations • 6 Nov 2019 • Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis
In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.
1 code implementation • 18 Sep 2019 • Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.
no code implementations • 10 Aug 2019 • Umit Rusen Aktas, Chao Zhao, Marek Kopicki, Ales Leonardis, Jeremy L. Wyatt
First, we present a simulator for generating and testing dexterous grasps.
no code implementations • NeurIPS 2018 • Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman, Josh Tenenbaum, Jiajun Wu
We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples.
no code implementations • 28 Nov 2018 • Steven McDonagh, Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, Gregory Slabaugh
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
no code implementations • 9 Oct 2018 • Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim, Federico Tombari, Kostas Bekris, Bertram Drost, Thibault Groueix, Krzysztof Walas, Vincent Lepetit, Ales Leonardis, Carsten Steger, Frank Michel, Caner Sahin, Carsten Rother, Jiri Matas
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
1 code implementation • 12 May 2018 • Grigorios Kalliatakis, Shoaib Ehsan, Ales Leonardis, Klaus McDonald-Maier
With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context.
no code implementations • 9 Nov 2017 • Grigorios Kalliatakis, Anca Sticlaru, George Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier
We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data.
no code implementations • ICCV 2017 • Pulak Purkait, Christopher Zach, Ales Leonardis
A vast majority of consumer cameras operate the rolling shutter mechanism, which often produces distorted images due to inter-row delay while capturing an image.
no code implementations • 24 Sep 2017 • Bruno Ferrarini, Shoaib Ehsan, Ales Leonardis, Naveed Ur Rehman, Klaus D. McDonald-Maier
Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.
no code implementations • 12 Mar 2017 • Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier
We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations.
no code implementations • 12 Mar 2017 • Grigorios Kalliatakis, Georgios Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Anca Sticlaru, Klaus D. McDonald-Maier
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention.
no code implementations • 19 Nov 2016 • Jingjing Xiao, Qiang Lan, Linbo Qiao, Ales Leonardis
Since each branch in NetT is trained by the videos of a specific category or groups of similar categories, NetT encodes category-based features for tracking.
no code implementations • 19 May 2016 • Shoaib Ehsan, Adrian F. Clark, Ales Leonardis, Naveed Ur Rehman, Klaus D. McDonald-Maier
Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed.
no code implementations • 19 May 2016 • Bruno Ferrarini, Shoaib Ehsan, Naveed Ur Rehman, Ales Leonardis, Klaus D. McDonald-Maier
The efficiency and the good accuracy in determining the optimal feature detector for any operating condition, make the proposed tool suitable to be utilized in real visual applications.
no code implementations • ICCV 2015 • Mete Ozay, Umit Rusen Aktas, Jeremy L. Wyatt, Ales Leonardis
We represent the topological relationship between shape components using graphs, which are aggregated to construct a hierarchical graph structure for the shape vocabulary.
no code implementations • CVPR 2015 • Jingjing Xiao, Rustam Stolkin, Ales Leonardis
This paper presents a method for single target tracking of arbitrary objects in challenging video sequences.
no code implementations • 4 Mar 2015 • Mete Ozay, Krzysztof Walas, Ales Leonardis
We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical object representation algorithm, namely Learned Hierarchy of Parts (LHOP).
no code implementations • 4 Mar 2015 • Matej Kristan, Jiri Matas, Ales Leonardis, Tomas Vojir, Roman Pflugfelder, Gustavo Fernandez, Georg Nebehay, Fatih Porikli, Luka Cehovin
This paper addresses the problem of single-target tracker performance evaluation.
1 code implementation • 21 Jan 2015 • Umit Rusen Aktas, Mete Ozay, Ales Leonardis, Jeremy L. Wyatt
A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP).
no code implementations • 23 Aug 2014 • Sanja Fidler, Marko Boben, Ales Leonardis
At the top-level of the vocabulary, the compositions are sufficiently large and complex to represent the whole shapes of the objects.
no code implementations • NeurIPS 2009 • Sanja Fidler, Marko Boben, Ales Leonardis
We explore and compare their computational behavior (space and time) and detection performance as a function of the number of learned classes on several recognition data sets.