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Multi-view Semantic Information Guidance for Light Field Image Segmentation

Multi-view Semantic Information Guidance for Light Field Ima...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Li, Yiming Cong, Ruixuan Wang, Sizhe Zhao, Mingyuan Zhang, Yang Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Beijing University of Chemical Technology College of Information Science and Technology Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

One of the great important fields of computer vision is semantic segmentation. As for single image semantic segmentation, due to limited available information, it appears poor performance when the occlusion and similar color interference occur, and has difficulty exploiting the rich scene information. In comparison, the special micro-len array structure of light field camera can record multi-view information of the scene, which provides us with a new solution to solve this issue. In this paper, we propose a multi-view semantic information guidance network (MSIGNet) for light field semantic segmentation. It can effectively utilize semantic information from multi-view images to guide pixel feature of center view image. First, we extract feature of each view image and further obtain semantic probability. Then all probabilities are aggregated through a self-adaptive multi-view probability fusion module. Last, the resulting coarse fusion representation interacts with center view feature to obtain the refined segmentation result. The proposed method shows excellent performance on both real-world and synthetic light field datasets. © 2023 IEEE.

关键词： Semantic Segmentation

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Surveillance Face Presentation Attack Detection Challenge

Surveillance Face Presentation Attack Detection Challenge

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Fang, Hao Liu, Ajian Wan, Jun Escalera, Sergio Escalante, Hugo Jair Lei, Zhen University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Chinese Academy of Sciences Mais Institute of Automation Beijing China Universitat de Barcelona Computer Vision Center Catalonia Barcelona Spain Óptica y Electrónica JInstituto Nacional de Astrofísica Puebla Mexico Cair Hkisi Cas

ISBN: (纸本)9798350302493

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. © 2023 IEEE.

关键词： Face recognition

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Relational Edge-Node Graph Attention Network for Classification of Micro-Expressions

Relational Edge-Node Graph Attention Network for Classificat...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Rakesh Kumar, Ankith Jain Bhanu, Bir University of California Department of Electrical and Computer Engineering Riverside United States

ISBN: (纸本)9798350302493

Facial micro-expressions (MEs) refer to subtle, transient, and involuntary muscle movements expressing a person's true feelings. This paper presents a novel two-stream relational edge-node graph attention network-based approach to classify MEs in a video by selecting the high-intensity frames and edge-node features that can provide valuable information about the relationship between nodes and structural information in a graph structure. The paper examines the impact of different edge-node features and their relationships on the graphs. The first step involves extracting high-intensity-emotion frames from the video using optical flow. Second, node feature embeddings are calculated using the node location coordinate features and the patch size information of the optical flow across each node location. Additionally, we obtain the global and local structural similarity score using the jaccard's similarity score and radial basis function as the edge features. Third, a self-attention graph pooling layer helps to remove the nodes with lower attention scores based on the top-k selection. As the final step, the network employs a two-stream edge-node graph attention network that focuses on finding correlations among the edge and node features, such as landmark coordinates, optical flow, and global and local edge features. A three-frame graph structure is designed to obtain spatiotemporal information. For 3 and 5 expression classes, the results are compared for SMIC and CASME II databases. © 2023 IEEE.

关键词： Optical flows

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Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Region-based Appearance and Flow Characteristics for Anomaly...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Gaus, Yona Falinie A. Bhowmik, Neelanjan Isaac-Medina, Brian K. S. Shum, Hubert P. H. Atapour-Abarghouei, Amir Breckon, Toby P. Durham University Department of Computer Science Durham United Kingdom Durham University Department of Engineering Durham United Kingdom

ISBN: (纸本)9798350302493

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance;2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the Long-Term infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches. © 2023 IEEE.

关键词： Anomaly detection

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TransER: Hybrid Model and Ensemble-based Sequential Learning for Non-homogenous Dehazing

TransER: Hybrid Model and Ensemble-based Sequential Learning...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Pennsylvania State University School of Electrical Engineering and Computer Science University Park United States

ISBN: (纸本)9798350302493

Image dehazing is one of the most challenging imaging inverse problems that estimates the haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent performance in handling both homogeneous and non-homogeneous dehazing problems, these networks are often trained end-to-end to estimate the haze-free image directly and require a large number of parameters. In this work, we propose a novel, lightweight two-stage deep network for non-homogeneous dehazing. In particular, our proposed method, denoted as TransER, consists of two separate deep neural networks which are TransConv Fusion Dehaze (TFD) model in Stage I and Lightweight Ensemble Reconstruction (LER) network in Stage II. The first model (TFD) using transformer-based encoder and decoders generates two estimates of the haze-free image: a parameter-based dehazed output based on the physical modeling of the problem and a pseudo haze-free output generated directly by the model in an end-to-end fashion. LER in stage II reconstructs the final dehazed output fusing the two estimates from stage I. We incorporate knowledge distillation to develop a teacher network with the same architecture as LER, allowing it to supervise the intermediate features. Extensive experiments performed on challenging real and synthetic scene image datasets (NTIRE 2019-2023, and RESIDE-indoor) demonstrate that TransER can outperform many state-of-the-art competing methods while using a significantly lower number of parameters. The source code is available at https://***/trungpsu1210/TransER. © 2023 IEEE.

关键词： Distillation

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Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection

Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Ob...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Yu, Kaicheng Tao, Tang Xie, Hongwei Lin, Zhiwei Liang, Tingting Wang, Bing Chen, Peng Hao, Dayang Wang, Yongtao Liang, Xiaodan Sun Yat-sen University Shenzhen Campus China Alibaba Group Autonomous Driving Lab China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350302493

To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse the camera and LiDAR information, has draw great research interest in recent years. As a common practice, people rely on large-scale datasets to fairly compare the performance of different methods. While these datasets have been carefully cleaned to ideally minimize any potential noise, we observe that they cannot truly reflect the data seen on a real autonomous vehicle, whose data tends to be noisy due to various reasons. This hinders the ability to simply estimate the robust performance under realistic noisy settings. To this end, we collect a series of real-world cases with noisy data distribution, and systematically formulate a robustness benchmark toolkit. It can simulate these cases on any clean dataset, which has the camera and LiDAR input modality. We showcase the effectiveness of our toolkit by establishing two novel robustness benchmarks on widely-adopted datasets, nuScenes and Waymo, then holistically evaluate the state-of-the-art fusion methods. We discover that: i) most fusion methods, when solely developed on these data, tend to fail inevitably when there is a disruption to the LiDAR input;ii) the improvement of the camera input is significantly inferior to the LiDAR one. We publish the robust fusion dataset, benchmark, detailed documents and instructions on https://***/robust-benchmark-website. © 2023 IEEE.

关键词： Cameras

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UDD: Dataset Distillation via Mining Underutilized Regions 7th

UDD: Dataset Distillation via Mining Underutilized Regions

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7th Chinese conference on pattern recognition and computer vision

作者： Wang, Shiguang Zhang, Zhongyu Cheng, Jian Univ Elect Sci & Technol China 2006 Xiyuan Ave Chengdu Peoples R China Tencent Technol Co Ltd Youtu Lab Shanghai Peoples R China

ISBN: (纸本)9789819787944;9789819787951

Dataset distillation synthesizes a small dataset such that a model trained on this set approximates the performance of the original dataset. Recent studies on dataset distillation focused primarily on the design of the optimization process, with methods such as gradient matching, feature alignment, and training trajectory matching. However, little attention has been given to the issue of underutilized regions in synthetic images. In this paper, we propose UDD, a novel approach to identify and exploit the underutilized regions to make them informative and discriminate, and thus improve the utilization of the synthetic dataset. Technically, UDD involves two underutilized regions searching policies for different conditions, i.e., response-based policy and data jittering-based policy. Compared with previous works, such two policies are utilization-sensitive, equipping with the ability to dynamically adjust the underutilized regions during the training process. Additionally, we analyze the current model optimization problem and design a category-wise feature contrastive loss, which can enhance the distinguishability of different categories and alleviate the shortcomings of the existing multi-formation methods. Experimentally, our method improves the utilization of the synthetic dataset and outperforms the state-of-the-art methods on various datasets, such as MNIST, FashionMNIST, SVHN, CIFAR-10, and CIFAR-100. For example, the improvements on CIFAR-10 and CIFAR-100 are 4.0 and 3.7% over the next best method with IPC = 1, by mining the underutilized regions.

关键词： Dataset Condensation Dataset Distillation Underutilized Region Image Synthesis

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EPI-Guided Cost Construction Network for Light Field Disparity Estimation

EPI-Guided Cost Construction Network for Light Field Dispari...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Tun Chen, Rongshan Cong, Ruixuan Yang, Da Cui, Zhenglong Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Recent learning-based light field (LF) disparity estimation methods construct cost volume by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets. They only use the visual information of SAIs and lose the geometry of LF. In this paper, we design a simple network that can cleverly integrate EPI features with cost volume to estimate the disparity. Firstly, we propose an efficient EPI extraction module to use abundant line characteristics. Secondly, we offer an EPI-Cost volume construction module that can create volume guided by the EPI line and the color consistency of images. Finally, after completing it, we adopt an inter-volume fusion module to considerably correlate the validity of EPI lines in both directions. Experimental results show the proposed method achieves state-of-the-art performance in the quantitative and qualitative evaluation of the UrbanLF-Syn dataset. © 2023 IEEE.

关键词： Cost estimating

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NTIRE 2022 Challenge on Perceptual Image Quality Assessment

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gu, Jinjin Cai, Haoming Dong, Chao Ren, Jimmy S. Timofte, Radu Gong, Yuan Lao, Shanshan Shi, Shuwei Wang, Jiahao Yang, Sidi Wu, Tianhe Xia, Weihao Yang, Yujiu Cao, Mingdeng Heng, Cong Fu, Lingzhi Zhang, Rongyu Zhang, Yusheng Wang, Hao Song, Hongjian Wang, Jing Fan, Haotian Hou, Xiaoxia Sun, Ming Li, Mading Zhao, Kai Yuan, Kun Kong, Zishang Wu, Mingda Zheng, Chuanchuan Conde, Marcos, V Burchi, Maxime Feng, Longtao Zhang, Tao Li, Yang Xu, Jingwen Wang, Haiqiang Liao, Yiting Li, Junlin Xu, Kele Sun, Tao Xiong, Yunsheng Keshari, Abhisek Komal, Komal Thakur, Sadbhawana Jakhetiya, Vinit Subudhi, Badri N. Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China SenseTime Res Beijing Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China UCL London England Netease Hangzhou Peoples R China Kuaishou Beijing Peoples R China Univ Wurzburg Comp Vis Lab Wurzburg Germany ByteDance Beijing Peoples R China Key Lab Parallel & Distributed Proc Beijing Peoples R China Indian Inst Technol Jammu Jagti Jammu & Kashmir India Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata W Bengal India Indian Inst Technol Jodhpur Jodhpur Rajasthan India Natl Taiwan Univ New Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate state-of-the-art performance.

关键词： Image quality computer vision conferences Computational modeling Market research Distortion pattern recognition

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Recursions Are All You Need: Towards Efficient Deep Unfolding Networks

Recursions Are All You Need: Towards Efficient Deep Unfoldin...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Alhejaili, Rawwad Alfarraj, Motaz Luqman, Hamzah Al-Shaikhi, Ali King Fahd University of Petroleum and Minerals Electrical Engineering Department Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals Information and Computer Science Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350302493

The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network. In this work, we propose a novel recursion-based framework to enhance the efficiency of deep unfolding models. First, recursions are used to effectively eliminate the redundancies in deep unfolding networks. Secondly, we randomize the number of recursions during training to decrease the overall training time. Finally, to effectively utilize the power of recursions, we introduce a learnable unit to modulate the features of the model based on both the total number of iterations and the current iteration index. To evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST. Extensive testing shows that our proposed framework allows the network to cut down as much as 75% of its learnable parameters while mostly maintaining its performance, and at the same time, it cuts around 21% and 42% from the training time for ISTA-Net+ and COAST respectively. Moreover, when presented with a limited training dataset, the recursive models match or even outperform their respective non-recursive baseline. Codes and pretrained models are available at https://***/Rawwad-Alhejaili/Recursions-Are-All-You-Need. © 2023 IEEE.

关键词： Redundancy

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