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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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SuperDisco: Super-Class Discovery Improves Visual recognition for the Long-Tail

SuperDisco: Super-Class Discovery Improves Visual Recognitio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yingjun Du Jiayi Shen Xiantong Zhen Cees G. M. Snoek AIM Lab University of Amsterdam Inception Institute of Artificial Intelligence

Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long-tailed recognition challenge, since they can learn the tail representation based on different levels of semantic abstraction, making the learned tail features more discriminative. This phenomenon motivated us to propose SuperDisco, an algorithm that discovers super-class representations for long-tailed recognition using a graph model. We learn to construct the super-class graph to guide the representation learning to deal with long-tailed distributions. Through message passing on the super-class graph, image representations are rectified and refined by attending to the most relevant entities based on the semantic similarity among their super-classes. Moreover, we propose to meta-learn the super-class graph under the supervision of a prototype graph constructed from a small amount of imbalanced data. By doing so, we obtain a more robust super-class graph that further improves the long-tailed recognition performance. The consistent state-of-the-art experiments on the long-tailed CIFAR-100, ImageNet, Places and iNaturalist demonstrate the benefit of the discovered super-class graph for dealing with long-tailed distributions.

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Reducing Domain Gap by Reducing Style Bias

Reducing Domain Gap by Reducing Style Bias

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

作者： Nam, Hyeonseob Lee, HyunJae Park, Jongchan Yoon, Wonjun Yoo, Donggeun Lunit Inc Seoul South Korea

ISBN: (纸本)9781665445092

Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.(1)

关键词： computer vision Adaptation models Shape Decision making Aerospace electronics Robustness Encoding

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$R^{2}$ Former: Unified Retrieval and Reranking Transformer for Place recognition

$R^{2}$ Former: Unified Retrieval and Reranking Transformer ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Sijie Zhu Linjie Yang Chen Chen Mubarak Shah Xiaohui Shen Heng Wang ByteDance Center for Research in Computer Vision University of Central Florida

Visual Place recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ignores other possible information that could be useful for reranking, e.g. local feature correlations, and attention values. In this paper, we propose a unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named $R^vision$ Former. The proposed reranking module takes feature correlation, attention value, and $xy$ coordinates into account, and learns to determine whether the image pair is from the same location. The whole pipeline is end-to-end trainable and the reranking module alone can also be adopted on other CNN or transformer backbones as a generic component. Remarkably, $R^vision$ Former significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption. It also achieves the state-of-the-art on the hold-out MSLS challenge set and could serve as a simple yet strong solution for real-world large-scale applications. Experiments also show vision transformer tokens are comparable and sometimes better than CNN local features on local matching. The code is released at https://***/Jeff-Zilence/R2Former.

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Quantum Permutation Synchronization

Quantum Permutation Synchronization

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

作者： Birdal, Tolga Golyanik, Vladislav Theobalt, Christian Guibas, Leonidas Stanford Univ Stanford CA 94305 USA Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9781665445092

We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unconstrained binary optimization problem (QUBO). While such formulation respects the binary nature of the problem, ensuring that the result is a set of permutations requires extra care. Hence, we: (i) show how to insert permutation constraints into a QUBO problem and (ii) solve the constrained QUBO problem on the current generation of the adiabatic quantum computers D-Wave. Thanks to the quantum annealing, we guarantee global optimality with high probability while sampling the energy landscape to yield confidence estimates. Our proofof-concepts realization on the adiabatic D-Wave computer demonstrates that quantum machines offer a promising way to solve the prevalent yet difficult synchronization problems.

关键词： computer vision Quantum algorithm Adiabatic Hardware pattern recognition Synchronization Optimization

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Prompt-Guided Zero-Shot Anomaly Action recognition using Pretrained Deep Skeleton Features

Prompt-Guided Zero-Shot Anomaly Action Recognition using Pre...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Fumiaki Sato Ryo Hachiuma Taiki Sekii Konica Minolta Inc.

This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and simultaneously addresses three limitations in the conventional skeleton-based approaches: target domain-dependent DNN training, robustness against skeleton errors, and a lack of normal samples. We present a unified, user prompt-guided zero-shot learning framework using a target domain-independent skeleton feature extractor, which is pretrained on a large-scale action recognition dataset. Particularly, during the training phase using normal samples, the method models the distribution of skeleton features of the normal actions while freezing the weights of the DNNs and estimates the anomaly score using this distribution in the inference phase. Additionally, to increase robustness against skeleton errors, we introduce a DNN architecture inspired by a point cloud deep learning paradigm, which sparsely propagates the features between Joints. Furthermore, to prevent the unobserved normal actions from being misidentified as abnormal actions, we incorporate a similarity score between the user prompt embeddings and skeleton features aligned in the common space into the anomaly score, which indirectly supplements normal actions. On two publicly available datasets, we conduct experiments to test the effectiveness of the proposed method with respect to above-mentioned limitations.

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MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes

MagDR: Mask-guided Detection and Reconstruction for Defendin...

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

作者： Chen, Zhikai Xie, Lingxi Pang, Shanmin He, Yong Zhang, Bo Xi An Jiao Tong Univ Xian Peoples R China Huawei Inc Shenzhen Peoples R China Tencent Blade Team Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide a learnable reconstruction procedure. Adaptive masks are extracted to capture the change in local facial regions. In experiments, MagDR defends three main tasks of deepfakes, and the learned reconstruction pipeline transfers across input data, showing promising performance in defending both black-box and white-box attacks.

关键词： Visualization Perturbation methods Pipelines Semantics Prediction algorithms pattern recognition Task analysis

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Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency

Self-Supervised Video GANs: Learning for Appearance Consiste...

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

作者： Hyun, Sangeek Kim, Jihwan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9781665445092

A video can be represented by the composition of appearance and motion. Appearance (or content) expresses the information invariant throughout time, and motion describes the time-variant movement. Here, we propose self-supervised approaches for video Generative Adversarial Networks (GANs) to achieve the appearance consistency and motion coherency in videos. Specifically, the dual discriminators for image and video individually learn to solve their own pretext tasks;appearance contrastive learning and temporal structure puzzle. The proposed tasks enable the discriminators to learn representations of appearance and temporal context, and force the generator to synthesize videos with consistent appearance and natural flow of motions. Extensive experiments in facial expression and human action public benchmarks show that our method outperforms the state-of-the-art video GANs. Moreover, consistent improvements regardless of the architecture of video GANs confirm that our framework is generic.

关键词： computer vision Force computer architecture Benchmark testing Generative adversarial networks Generators pattern recognition

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iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-training for Visual recognition

iCLIP: Bridging Image Classification and Contrastive Languag...

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conference on computer vision and pattern recognition (cvpr)

作者： Yixuan Wei Yue Cao Zheng Zhang Houwen Peng Zhuliang Yao Zhenda Xie Han Hu Baining Guo Tsinghua University Microsoft Research Asia

This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naïve multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and mean-while surpasses CLIP by 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https://***/weiyx16/iCLIP.

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3D Registration with Maximal Cliques

3D Registration with Maximal Cliques

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conference on computer vision and pattern recognition (cvpr)

作者： Xiyu Zhang Jiaqi Yang Shikun Zhang Yanning Zhang School of Computer Science Northwestern Polytechnical University China

As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration method with maximal cliques (MAC). The key insight is to loosen the previous maximum clique constraint, and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each of which represents a consensus set. We perform node-guided clique selection then, where each node corresponds to the maximal clique with the greatest graph weight. 3) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration. Extensive experiments on U3M, 3DMatch, 3DLoMatch and KITTI demonstrate that MAC effectively increases registration accuracy, outperforms various state-of-the-art methods and boosts the performance of deep-learned methods. MAC combined with deep-learned methods achieves state-of-the-art registration recall of 95.7% /78.9% on 3DMatch /3DLoMatch.

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EnD: Entangling and Disentangling deep representations for bias correction

EnD: Entangling and Disentangling deep representations for b...

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

作者： Tartaglione, Enzo Barbano, Carlo Alberto Grangetto, Marco Univ Turin Comp Sci Dept Turin Italy

ISBN: (纸本)9781665445092

Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose EnD, a regularization strategy whose aim is to prevent deep models from learning unwanted biases. In particular, we insert an "information bottleneck" at a certain point of the deep neural network, where we disentangle the information about the bias, still letting the useful information for the training task forward-propagating in the rest of the model. One big advantage of EnD is that it does not require additional training complexity (like decoders or extra layers in the model), since it is a regularizer directly applied on the trained model. Our experiments show that EnD effectively improves the generalization on unbiased test sets, and it can be effectively applied on realcase scenarios, like removing hidden biases in the COVID19 detection from radiographic images.

关键词： Training COVID-19 Radiography Deep learning computer vision Training data Data models

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