| Records |
| Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Jin Kim; Dogun Kim; Zhihao Li; Yingchun Jian; Bo Yan; Leilei Cao; Fengliang Qi; Hongbin Wang Rongyuan Wu; Lingchen Sun; Yongqiang Zhao; Lin Li; Kai Wang; Yicheng Wang; Xuanming Zhang; Huiyuan Wei; Chonghua Lv; Qigong Sun; Xiaolin Tian; Zhuang Jia; Jiakui Hu; Chenyang Wang; Zhiwei Zhong; Xianming Liu; Junjun Jiang |
| Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2022 |
Type |
Conference Article |
| Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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418-426 |
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This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. |
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New Orleans; USA; June 2022 |
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CVPRW |
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MSIAU; no menciona |
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no |
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Admin @ si @ RSV2022c |
Serial |
3775 |
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| Author |
Francesco Pelosin; Saurav Jha; Andrea Torsello; Bogdan Raducanu; Joost Van de Weijer |
| Title |
Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization |
Type |
Conference Article |
| Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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Learning systems; Weight measurement; Image recognition; Surgery; Benchmark testing; Transformers; Stability analysis |
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In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) – while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners. 1 |
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New Orleans; USA; June 2022 |
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CVPRW |
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LAMP; 600.147 |
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no |
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Admin @ si @ PJT2022 |
Serial |
3784 |
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Hector Laria Mantecon; Yaxing Wang; Joost Van de Weijer; Bogdan Raducanu |
| Title |
Transferring Unconditional to Conditional GANs With Hyper-Modulation |
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Conference Article |
| Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. |
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New Orleans; USA; June 2022 |
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CVPRW |
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LAMP; 600.147; 602.200 |
Approved |
no |
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LWW2022a |
Serial |
3785 |
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Yawei Li; Yulun Zhang; Radu Timofte; Luc Van Gool; Zhijun Tu; Kunpeng Du; Hailing Wang; Hanting Chen; Wei Li; Xiaofei Wang; Jie Hu; Yunhe Wang; Xiangyu Kong; Jinlong Wu; Dafeng Zhang; Jianxing Zhang; Shuai Liu; Furui Bai; Chaoyu Feng; Hao Wang; Yuqian Zhang; Guangqi Shao; Xiaotao Wang; Lei Lei; Rongjian Xu; Zhilu Zhang; Yunjin Chen; Dongwei Ren; Wangmeng Zuo; Qi Wu; Mingyan Han; Shen Cheng; Haipeng Li; Ting Jiang; Chengzhi Jiang; Xinpeng Li; Jinting Luo; Wenjie Lin; Lei Yu; Haoqiang Fan; Shuaicheng Liu; Aditya Arora; Syed Waqas Zamir; Javier Vazquez; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong; Jinhui Tang; Bo Yang; Jingxiang Chen; Chenghua Li; Xi Zhang; Zhao Zhang; Jiahuan Ren; Zhicheng Ji; Kang Miao; Suiyi Zhao; Huan Zheng; YanYan Wei; Kangliang Liu; Xiangcheng Du; Sijie Liu; Yingbin Zheng; Xingjiao Wu; Cheng Jin; Rajeev Irny; Sriharsha Koundinya; Vighnesh Kamath; Gaurav Khandelwal; Sunder Ali Khowaja; Jiseok Yoon; Ik Hyun Lee; Shijie Chen; Chengqiang Zhao; Huabin Yang; Zhongjian Zhang; Junjia Huang; Yanru Zhang |
| Title |
NTIRE 2023 challenge on image denoising: Methods and results |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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Pages |
1904-1920 |
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This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising. |
| Address |
Vancouver; Canada; June 2023 |
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CVPRW |
| Notes |
MACO; CIC |
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no |
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Admin @ si @ LZT2023 |
Serial |
3910 |
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| Author |
Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
| Title |
Multi-Modal Aerial View Image Challenge: Translation From Synthetic Aperture Radar to Electro-Optical Domain Results-PBVS 2023 |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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515-523 |
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This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations. |
| Address |
Vancouver; Canada; June 2023 |
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CVPRW |
| Notes |
MSIAU |
Approved |
no |
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Admin @ si @ LNS2023a |
Serial |
3913 |
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| Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Chenyang Wang; Junjun Jiang; Xianming Liu; Zhiwei Zhong; Dai Bin; Li Ruodi; Li Shengye |
| Title |
Thermal Image Super-Resolution Challenge Results-PBVS 2023 |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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470-478 |
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This paper presents the results of two tracks from the fourth Thermal Image Super-Resolution (TISR) challenge, held at the Perception Beyond the Visible Spectrum (PBVS) 2023 workshop. Track-1 uses the same thermal image dataset as previous challenges, with 951 training images and 50 validation images at each resolution. In this track, two evaluations were conducted: the first consists of generating a SR image from a HR thermal noisy image downsampled by four, and the second consists of generating a SR image from a mid-resolution image and compare it with its semi-registered HR image (acquired with another camera). The results of Track-1 outperformed those from last year’s challenge. On the other hand, Track-2 uses a new acquired dataset consisting of 160 registered visible and thermal images of the same scenario for training and 30 validation images. This year, more than 150 teams participated in the challenge tracks, demonstrating the community’s ongoing interest in this topic. |
| Address |
Vancouver; Canada; June 2023 |
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CVPRW |
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MSIAU |
Approved |
no |
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Admin @ si @ RSV2023 |
Serial |
3914 |
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| Author |
Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
| Title |
Multi-Modal Aerial View Object Classification Challenge Results-PBVS 2023 |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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412-421 |
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This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognitioN (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness.The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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MSIAU |
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no |
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Admin @ si @ LNS2023b |
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3915 |
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| Author |
Chenshen Wu; Joost Van de Weijer |
| Title |
Density Map Distillation for Incremental Object Counting |
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Conference Article |
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2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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2505-2514 |
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We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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LAMP |
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no |
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Admin @ si @ WuW2023 |
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3916 |
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| Author |
Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei |
| Title |
Surveillance Face Presentation Attack Detection Challenge |
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Conference Article |
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2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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6360-6370 |
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Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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HuPBA |
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no |
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Admin @ si @ FLW2023 |
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3917 |
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Galadrielle Humblot-Renaux; Sergio Escalera; Thomas B. Moeslund |
| Title |
Beyond AUROC & co. for evaluating out-of-distribution detection performance |
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Conference Article |
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2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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3880-3889 |
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While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric – Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc |
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Vancouver; Canada; June 2023 |
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CVPRW |
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HUPBA |
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no |
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Admin @ si @ HEM2023 |
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3918 |
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Dong Wang; Jia Guo; Qiqi Shao; Haochi He; Zhian Chen; Chuanbao Xiao; Ajian Liu; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Jun Wan; Jiankang Deng |
| Title |
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results |
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Conference Article |
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2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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6379-6390 |
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Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface 1 . |
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Vancouver; Canada; June 2023 |
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CVPRW |
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HUPBA |
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no |
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Admin @ si @ WGS2023 |
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3919 |
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| Author |
Bogdan Raducanu; Jordi Vitria |
| Title |
Online Learning for Human-Robot Interaction |
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Conference Article |
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2007 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshop on |
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Minneapolis (USA) |
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CVPR |
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OR; MV |
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no |
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BCNPCL @ bcnpcl @ RaV2007a |
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791 |
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Sergio Escalera; Petia Radeva; Oriol Pujol |
| Title |
Complex Salient Regions for Computer Vision Problems |
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2007 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshop on |
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Minneapolis (USA) |
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BCNPCL @ bcnpcl @ ERP2007 |
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908 |
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| Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
| Title |
IVUS Tissue Characterization with Sub-class Error-correcting Output Codes |
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Conference Article |
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2008 |
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Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on, pp. 1–8, 23–28 juny 2008. |
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Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on Radio Frequency, texture-based, slope-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different subsets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers and feature sets. |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPM2008 |
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1041 |
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Agata Lapedriza; David Masip; Jordi Vitria |
| Title |
On the Use of Independent Tasks for Face Recognition |
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Conference Article |
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2008 |
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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1–6 |
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OR; MV |
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no |
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BCNPCL @ bcnpcl @ LMV2008b |
Serial |
1043 |
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