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WSRD: A Novel Benchmark for High Resolution Image Shadow Removal

WSRD: A Novel Benchmark for High Resolution Image Shadow Rem...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Florin-Alexandru Vasluianu Tim Seizinger Radu Timofte Computer Vision Laboratory CAIDAS & IFI University of Würzburg Germany

Shadow removal is an important computer vision task, whose aim is to successfully detect the shadow affected area appearing through light occlussion, followed by a photorealistic restoration of the affected image contents, textures, and details. After decades of research, a multitude of hand-crafted restoration techniques were proposed, following different observations on shadow formation models, with scenes altered in particular conditions. However, the increased popularity of deep learning based solutions enabled a significant step forward for the shadow removal solutions, both in terms of reconstruction fidelity and perceptual properties. However, the publicly available datasets remain focused around a particularly low complexity setup, with a low variety of light occluders and affected backgrounds, and with limited representation for more complex light interactions and complex shadow patterns. In this work, we propose WSRD, a novel benchmark for high resolution image shadow removal, characterized by a large variety in terms or represented objects, backgrounds and light occluders. We study more complex interactions, combining self shadows with externally casted shadows, to further extend the study of the phenomenon, its factors and effects. To prove WSRD as a relevant benchmark, we propose DNSR, a novel shadow removal method, comparing the results on WSRD with the performance level observed on other well-established benchmarks like ISTD and ISTD+. We validate our approach comparing with existing state-of-the-art (SOTA) methods, improving both in reconstruction fidelity and perceptual properties, setting a new SOTA for the field.

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CrisisHateMM: Multimodal Analysis of Directed and Undirected Hate Speech in Text-Embedded Images from Russia-Ukraine Conflict

CrisisHateMM: Multimodal Analysis of Directed and Undirected...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Aashish Bhandari Siddhant B. Shah Surendrabikram Thapa Usman Naseem Mehwish Nasim Department of CSE Delhi Technological University India Department of Computer Science Virginia Tech USA School of Computer Science The University of Sydney Australia School of Physics Mathematics and Computing The University of Western Australia Australia College of Science and Engineering Flinders University Australia

Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine war, both sides used text-embedded images extensively to spread propaganda and hate speech. To aid in moderating such content, this paper introduces CrisisHateMM, a novel multimodal dataset of over 4,700 text-embedded images from the Russia-Ukraine conflict, annotated for hate and non-hate speech. The hate speech is annotated for directed and undirected hate speech, with directed hate speech further annotated for individual, community, and organizational targets. We benchmark the dataset using unimodal and multimodal algorithms, providing insights into the effectiveness of different approaches for detecting hate speech in text-embedded images. Our results show that multimodal approaches outperform unimodal approaches in detecting hate speech, highlighting the importance of combining visual and textual features. This work provides a valuable resource for researchers and practitioners in automated content moderation and social media analysis. The CrisisHateMM dataset and codes are made publicly available at https://***/aabhandari/CrisisHateMM.

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Multi-Modal Fusion of Event and RGB for Monocular Depth Estimation Using a Unified Transformer-based Architecture

Multi-Modal Fusion of Event and RGB for Monocular Depth Esti...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anusha Devulapally Md Fahim Faysal Khan Siddharth Advani Vijaykrishnan Narayanan The Pennsylvania State University Samsung Electronics America

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of robotics and autonomous navigation, accurate pixel-level depth estimation has gained significant importance. Event cameras or dynamic vision sensors, capture asynchronous changes in brightness at the pixel level, offering benefits such as high temporal resolution, no motion blur, and a wide dynamic range. However, unlike traditional cameras that measure absolute intensity, event cameras lack the ability to provide scene context. Efficiently combining the advantages of both asynchronous events and synchronous RGB images to enhance depth estimation remains a challenge. In our study, we introduce a unified transformer that combines both event and RGB modalities to achieve precise depth prediction. In contrast to individual transformers for input modalities, a unified transformer model captures inter-modal dependencies and uses self-attention to enhance event-RGB contextual interactions. This approach exceeds the performance of recurrent neural network (RNN) methods used in state-of-the-art models. To encode the temporal information from events, convLSTMs are used before the transformer to improve depth estimation. Our proposed architecture outperforms the existing approaches in terms of absolute mean depth error, achieving state-of-the-art results in most cases. Additionally, the performance is also seen in other metrics like RMSE, absolute relative difference and depth thresholds compared to the existing approaches. The source code is available at: https://***/anusha-devulapally/ER-F2D.

关键词： Measurement computer vision Accuracy Recurrent neural networks Robot vision systems Estimation computer architecture

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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ajian Liu Zichang Tan Yanyan Liang Jun Wan MAIS Institute of Automation Chinese Academy of Sciences Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack pattern Generation (Adv-APG) module and a Supervised learning-based Attack pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets.

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DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera

DemosaicFormer: Coarse-to-Fine Demosaicing Network for Hybri...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Senyan Xu Zhijing Sun Jiaying Zhu Yurui Zhu Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hybrid Event-Based vision Sensor (HybridEVS) is a novel sensor integrating traditional frame-based and event-based sensors, offering substantial benefits for applications requiring low-light, high dynamic range, and low-latency environments, such as smartphones and wearable devices. Despite its potential, the lack of Image signal processing (ISP) pipeline specifically designed for HybridEVS poses a significant challenge. To address this challenge, in this study, we propose a coarse-to-fine framework named DemosaicFormer which comprises coarse demosaicing and pixel correction. Coarse demosaicing network is designed to produce a preliminary high-quality estimate of the RGB image from the HybridEVS raw data while the pixel correction network enhances the performance of image restoration and mitigates the impact of defective pixels. Our key innovation is the design of a Multi-Scale Gating Module (MSGM) applying the integration of cross-scale features, which allows feature information to flow between different scales. Additionally, the adoption of progressive training and data augmentation strategies further improves model’s robustness and effectiveness. Experimental results show superior performance against the existing methods both qualitatively and visually, and our DemosaicFormer achieves the best performance in terms of all the evaluation metrics in the MIPI 2024 challenge on Demosaic for Hybridevs Camera. The code is available at this repository.

关键词： Training Measurement Technological innovation vision sensors Cameras Data augmentation Robustness

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A Temporal Encoder-Decoder Approach to Extracting Blood Volume Pulse Signal Morphology from Face Videos

A Temporal Encoder-Decoder Approach to Extracting Blood Volu...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Fulan Li Surendrabikram Thapa Shreyas Bhat Abhijit Sarkar A. Lynn Abbott Bradley Department of Electrical and Computer Engineering Virginia Tech USA Department of Computer Science Virginia Tech USA Virginia Tech Transportation Institute USA

This paper considers methods for extracting blood volume pulse (BVP) representations from video of the human face. Whereas most previous systems have been concerned with estimating vital signs such as average heart rate, this paper addresses the more difficult problem of recovering BVP signal morphology. We present a new approach that is inspired by temporal encoder-decoder architectures that have been used for audio signal separation. As input, this system accepts a temporal sequence of RGB (red, green, blue) values that have been spatially averaged over a small portion of the face. The output of the system is a temporal sequence that approximates a BVP signal. In order to reduce noise in the recovered signal, a separate processing step extracts individual pulses and performs normalization and outlier removal. After these steps, individual pulse shapes have been extracted that are sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area. The source code is available at https://***/Adleof/CVPM-2023-Temporal-Encoder-Decoder-iPPG.

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NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Taehoon Kim Pyunghwan Ahn Sangyun Kim Sihaeng Lee Mark Marsden Alessandra Sala Seung Hwan Kim Bohyung Han Kyoung Mu Lee Honglak Lee Kyounghoon Bae Xiangyu Wu Yi Gao Hailiang Zhang Yang Yang Weili Guo Jianfeng Lu Youngtaek Oh Jae Won Cho Dong-Jin Kim In So Kweon Junmo Kim Wooyoung Kang Won Young Jhoo Byungseok Roh Jonghwan Mun Solgil Oh Kenan Emir Ak Gwang-Gook Lee Yan Xu Mingwei Shen Kyomin Hwang Wonsik Shin Kamin Lee Wonhark Park Dongkwan Lee Nojun Kwak Yujin Wang Yimu Wang Tiancheng Gu Xingchang Lv Mingmao Sun

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project 1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Training Adaptation models Visualization computer vision Computational modeling conferences Training data

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SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations

SciFlow: Empowering Lightweight Optical Flow Models with Sel...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jamie Menjay Lin Jisoo Jeong Hong Cai Risheek Garrepalli Kai Wang Fatih Porikli Qualcomm Technologies Inc. Qualcomm AI Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent advancements, achieving real-time on-device optical flow estimation remains a complex challenge. First, an optical flow model must be sufficiently lightweight to meet computation and memory constraints to ensure real-time performance on devices. Second, the necessity for real-time on-device operation imposes constraints that weaken the model’s capacity to adequately handle ambiguities in flow estimation, thereby intensifying the difficulty of preserving flow *** paper introduces two synergistic techniques, Self-Cleaning Iteration (SCI) and Regression Focal Loss (RFL), designed to enhance the capabilities of optical flow models, with a focus on addressing optical flow regression ambiguities. These techniques prove particularly effective in mitigating error propagation, a prevalent issue in optical flow models that employ iterative refinement. Notably, these techniques add negligible to zero overhead in model parameters and inference latency, thereby preserving real-time on-device *** effectiveness of our proposed SCI and RFL techniques, collectively referred to as SciFlow for brevity, is demonstrated across two distinct lightweight optical flow model architectures in our experiments. Remarkably, SciFlow enables substantial reduction in error metrics (EPE and Fl-all) over the baseline models by up to 6.3% and 10.5% for in-domain scenarios and by up to 6.2% and 13.5% for cross-domain scenarios on the Sintel and KITTI 2015 datasets, respectively.

关键词： Optical losses Training Performance evaluation computer vision Image motion analysis Computational modeling Estimation

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sami Barchid José Mennesson Chaabane Djéraba Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL IMT Nord Europe Institut Mines-Télécom Univ. Lille Centre for Digital Systems Lille France

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach.

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Unified Physical-Digital Attack Detection Challenge

Unified Physical-Digital Attack Detection Challenge

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Haocheng Yuan Ajian Liu Junze Zheng Jun Wan Jiankang Deng Sergio Escalera Hugo Jair Escalante Isabelle Guyon Zhen Lei M.U.S.T MAIS CASIA China SAI UCAS China ICL UK CVC Spain INAOE CINVESTAV Mexico ChaLearn USA CAIR HKISI CAS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face Anti-Spoofing (FAS) is crucial to safeguard Face recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which poses significant limitations in real-world scenarios where FR systems face hybrid physical-digital threats. To facilitate the research of Unified Attack Detection (UAD) algorithms, a large-scale UniAttackData dataset has been collected. UniAttackData is the largest public dataset for Unified Attack Detection, with a total of 28,706 videos, where each unique identity encompasses all advanced attack types. Based on this dataset, we organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections. It attracted 136 teams for the development phase, with 13 qualifying for the final round. The results re-verified by the organizing team were used for the final ranking. This paper comprehensively reviews the challenge, detailing the dataset introduction, protocol definition, evaluation criteria, and a summary of published results. Finally, we focus on the detailed analysis of the highest-performing algorithms and offer potential directions for unified physical-digital attack detection inspired by this competition. Challenge Website: https://***/view/face-anti-spoofing-challenge/welcome/challengecvpr2024

关键词： Measurement computer vision Protocols Codes Reviews Face recognition conferences

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