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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing

Perception Matters: Detecting Perception Failures of VQA Mod...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yuan, Yuanyuan Wang, Shuai Jiang, Mingyue Chen, Tsong Yueh HKUST Hong Kong Peoples R China Zhejiang Sci Tech Univ Hangzhou Peoples R China Swinburne Univ Technol Hawthorn Vic Australia

ISBN: (纸本)9781665445092

Visual question answering (VQA) takes an image and a natural-language question as input and returns a natural-language answer. To date, VQA models are primarily assessed by their accuracy on high-level reasoning questions. Nevertheless, Given that perception tasks (e.g., recognizing objects) are the building blocks in the compositional process required by high-level reasoning, there is a demanding need to gain insights into how much of a problem lowlevel perception is. Inspired by the principles of software metamorphic testing, we introduce MetaVQA, a model-agnostic framework for benchmarking perception capability of VQA models. Given an image i, MetaVQA is able to synthesize a low-level perception question q. It then jointly transforms (i, q) to one or a set of sub-questions and subimages. MetaVQA checks whether the answer to (i, q) satisfies metamorphic relationships (MRs), denoting perception consistency, with the composed answers of transformed questions and images. Violating MRs denotes a failure of answering perception questions. MetaVQA successfully detects over 4.9 million perception failures made by popular VQA models with metamorphic testing. The state-of-the-art VQA models (e.g., the champion of VQA 2020 Challenge) suffer from perception consistency problems. In contrast, the Oscar VQA models, by using anchor points to align questions and images, show generally better consistency in perception tasks. We hope MetaVQA will revitalize interest in enhancing the low-level perceptual abilities of VQA models, a cornerstone of high-level reasoning.

关键词： Visualization computer vision Computational modeling Transforms Benchmark testing Knowledge discovery Cognition

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Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring 30

Deep Multi-scale Convolutional Neural Network for Dynamic Sc...

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Nah, Seungjun Kim, Tae Hyun Lee, Kyoung Mu Seoul Natl Univ Dept ECE ASRI Seoul 151742 South Korea

ISBN: (纸本)9781538604571

Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, scene depth variation. To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear. Moreover, recent machine learning based methods also depend on synthetic blur datasets generated under these assumptions. This makes conventional deblurring methods fail to remove blurs where blur kernel is difficult to approximate or parameterize (e.g. object motion boundaries). In this work, we propose a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources. Together, we present multi-scale loss function that mimics conventional coarse-to-fine approaches. Furthermore, we propose a new large-scale dataset that provides pairs of realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera. With the proposed model trained on this dataset, we demonstrate empirically that our method achieves the state-of-the-art performance in dynamic scene deblurring not only qualitatively, but also quantitatively.

关键词： Kernel Cameras Estimation Dynamics Training Image restoration computer architecture

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Image2Audio: Facilitating Semi-supervised Audio Emotion recognition with Facial Expression Image

Image2Audio: Facilitating Semi-supervised Audio Emotion Reco...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： He, Gewen Liu, Xiaofeng Fan, Fangfang You, Jane Florida State Univ Tallahassee FL 32306 USA Harvard Univ Cambridge MA 02138 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA HK PolyU Hong Kong Peoples R China

ISBN: (纸本)9781728193601

There is a large amount of public available labeled image-based facial expression recognition datasets. How could these images help for the audio emotion recognition with limited labeled data according to their inherent correlations can be a meaningful and challenging task. In this paper, we propose a semi-supervised adversarial network that allows the knowledge transfer from the labeled videos to the heterogeneous labeled audio domain hence enhancing the audio emotion recognition performance. Specifically, face image samples are translated to the spectrograms class-wisely. To harness the translated samples in a sparsely distributed area and construct a tighter decision boundary, we propose to precisely estimate the density on feature space and incorporate the reliable low-density sample with an annealing scheme. Moreover, the unlabeled audios are collected with the high-density path in a graph representation. As a possible "recognition via generation" framework, we empirically demonstrated its effectiveness on several audio emotional recognition benchmarks.

关键词： Emotion recognition

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Dual Memory Networks: A Versatile Adaptation Approach for vision-Language Models

Dual Memory Networks: A Versatile Adaptation Approach for Vi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Yabin Zhu, Wenjie Tang, Hui Ma, Zhiyuan Zhou, Kaiyang Zh, Lei HKPolyU Hong Kong Peoples R China OPPO Hong Kong Peoples R China HKUST Hong Kong Peoples R China HKBU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent research. The adaptation strategies can be typically categorized into three paradigms: zero-shot adaptation, few-shot adaptation, and the recently-proposed training-free few-shot adaptation. Most existing approaches are tailored for a specific setting and can only cater to one or two of these paradigms. In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings. Specifically, we propose the dual memory networks that comprise dynamic and static memory components. The static memory caches training data knowledge, enabling training-free few-shot adaptation, while the dynamic memory preserves historical test features online during the testing process, allowing for the exploration of additional data insights beyond the training set. This novel capability enhances model performance in the few-shot setting and enables model usability in the absence of training data. The two memory networks employ the same flexible memory interactive strategy, which can operate in a training-free mode and can be further enhanced by incorporating learnable projection layers. Our approach is tested across 11 datasets under the three task settings. Remarkably, in the zero-shot scenario, it outperforms existing methods by over 3% and even shows superior results against methods utilizing external training data. Additionally, our method exhibits robust performance against natural distribution shifts. Codes are available at https://***/YBZh/DMN.

关键词： dual memory networks versatile adaptation vision-language models

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SpotTune: Transfer Learning through Adaptive Fine-tuning 32

SpotTune: Transfer Learning through Adaptive Fine-tuning

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Guo, Yunhui Shi, Honghui Kumar, Abhishek Grauman, Kristen Rosing, Tajana Feris, Rogerio IBM Res Yorktown Hts NY 10598 USA MIT IBM Watson AI Lab Cambridge MA 02142 USA Univ Calif San Diego San Diego CA 92103 USA Univ Texas Austin Austin TX 78712 USA Google Brain Mountain View CA USA

ISBN: (纸本)9781728132938

Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning with deep neural networks is to fine-tune a model pretrained on the source task using data from the target task. In this paper, we propose an adaptive fine-tuning approach, called SpotTune, which finds the optimal fine-tuning strategy per instance for the target data. In SpotTune, given an image from the target task, a policy network is used to make routing decisions on whether to pass the image through the fine-tuned layers or the pre-trained layers. We conduct extensive experiments to demonstrate the effectiveness of the proposed approach. Our method outperforms the traditional fine-tuning approach on 12 out of 14 standard datasets. We also compare SpotTune with other state-of-the-art fine-tuning strategies, showing superior performance. On the Visual Decathlon datasets, our method achieves the highest score across the board without bells and whistles.

关键词： Categorization Deep Learning Optimization Methods recognition: Detection Retrieval

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COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification

COCAS: A Large-Scale Clothes Changing Person Dataset for Re-...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Shijie Li, Shihua Chen, Dapeng Zhao, Rui Yan, Junjie Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Microelect Beijing Peoples R China

ISBN: (纸本)9781728171685

Recent years have witnessed great progress in person reidentification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote the re-id research. To our best knowledge, all the existing benchmarks assume the same person will have the same clothes. While in real-world scenarios, it is very often for a person to change clothes. To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named ClOthes ChAnging Person Set (COCAS), which provides multiple images of the same identity with different clothes. COCAS totally contains 62,382 body images from 5,266 persons. Based on COCAS, we introduce a new person re-id setting for clothes changing problem, where the query includes both a clothes template and a person image taking another clothes. Moreover, we propose a two-branch network named Biometric-Clothes Network (BC-Net) which can effectively integrate biometric and clothes feature for re-id under our setting. Experiments show that it is feasible for clothes changing re-id with clothes templates.

关键词： Large dataset

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Multi-Robot Active Mapping via Neural Bipartite Graph Matching

Multi-Robot Active Mapping via Neural Bipartite Graph Matchi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ye, Kai Dong, Siyan Fan, Qingnan Wang, He Yi, Li Xia, Fei Wang, Jue Chen, Baoquan Peking Univ Beijing Peoples R China Shandong Univ Jinan Peoples R China Tencent AI Lab Bellevue WA 98004 USA Tsinghua Univ Beijing Peoples R China Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimation to enable more efficient robot movements. Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction. In this paper, we propose a novel algorithm, namely NeuralCoMapping, which takes advantage of both approaches. We reduce the problem to bipartite graph matching, which establishes the node correspondences between two graphs, denoting robots and frontiers. We introduce a multiplex graph neural network (mGNN) that learns the neural distance to fill the affinity matrix for more effective graph matching. We optimize the mGNN with a differentiable linear assignment layer by maximizing the long-term values that favor time efficiency and map completeness via reinforcement learning. We compare our algorithm with several state-of-the-art multi-robot active mapping approaches and adapted reinforcement-learning baselines. Experimental results demonstrate the superior performance and exceptional generalization ability of our algorithm on various indoor scenes and unseen number of robots, when only trained with 9 indoor scenes.

关键词： Multiplexing Three-dimensional displays Machine vision Robot vision systems Estimation Reinforcement learning Sensor systems and applications

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Neural Aggregation Network for Video Face recognition 30

Neural Aggregation Network for Video Face Recognition

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Jiaolong Ren, Peiran Zhang, Dongqing Chen, Dong Wen, Fang Li, Hongdong Hua, Gang Microsoft Res Redmond WA 98052 USA Australian Natl Univ Canberra ACT Australia Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9781538604571

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.

关键词： Facial recognition Eigenvector Face Face Videotapes images convex hull agglomeration Imagery (Psychotherapy) Invariant Persons

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Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation 32

Joint Representation and Estimator Learning for Facial Actio...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Yong Wu, Baoyuan Dong, Weiming Li, Zhifeng Liu, Wei Hu, Bao-Gang Ji, Qiang Tencent AI Lab Bellevue WA 98004 USA CASIA Natl Lab Pattern Recognit Beijing Peoples R China Rensselaer Polytech Inst Troy NY 12181 USA

ISBN: (纸本)9781728132938

Facial action unit (AU) intensity is an index to characterize human expressions. Accurate AU intensity estimation depends on three major elements: image representation, intensity estimator, and supervisory information. Most existing methods learn intensity estimator with fixed image representation, and rely on the availability of fully annotated supervisory information. In this paper, a novel general framework for AU intensity estimation is presented, which differs from traditional estimation methods in two aspects. First, rather than keeping image representation fixed, it simultaneously learns representation and intensity estimator to achieve an optimal solution. Second, it allows incorporating weak supervisory training signal from human knowledge (e.g. feature smoothness, label smoothness, label ranking, and positive label), which makes our model trainable even fully annotated information is not available. More specifically, human knowledge is represented as either soft or hard constraints which are encoded as regularization terms or equality/inequality constraints, respectively. On top of our novel framework, we additionally propose an efficient algorithm for optimization based on Alternating Direction Method of Multipliers (ADMM). Evaluations on two benchmark databases show that our method outperforms competing methods under different ratios of AU intensity annotations, especially for small ratios.

关键词： And Body Pose Biometrics Face Gesture

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Tell Me Where to Look: Guided Attention Inference Network 31

Tell Me Where to Look: Guided Attention Inference Network

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Kunpeng Wu, Ziyan Peng, Kuan-Chuan Ernst, Jan Fu, Yun Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Comp & Informat Sci Boston MA 02115 USA Siemens Corp Technol Princeton NJ USA

ISBN: (纸本)9781538664209

Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then available as priors for tasks such as object localization and semantic segmentation. In one common framework we address three shortcomings of previous approaches in modeling such attention maps: We (1) make attention maps an explicit and natural component of the end-to-end training for the first time, (2) provide self-guidance directly on these maps by exploring supervision from the network itself to improve them, and (3) seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. We clearly surpass the state-of-the-art on PASCAL VOC 2012 test and val. sets. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing weakly supervised learners to improve their generalization performance.

关键词： Task analysis Training Semantics Visualization Training data computer vision Supervised learning

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