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OpenGait: Revisiting Gait recognition Toward Better Practicality

OpenGait: Revisiting Gait Recognition Toward Better Practica...

引用

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

作者： Fan, Chao Liang, Junhao Shen, Chuanfu Hou, Saihui Huang, Yongzhen Yu, Shiqi Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China WATRIX AI Beijing Peoples R China

ISBN: (纸本)9798350301298

Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly,we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, GaitBase. Experimentally, we comprehensively compare GaitBase with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://***/ShiqiYu/OpenGait.

关键词： Biometrics

来源：评论

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Dense Distinct Query for End-to-End Object Detection

Dense Distinct Query for End-to-End Object Detection

引用

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

作者： Zhang, Shilong Wang, Xinjiang Wang, Jiaqi Pang, Jiangmiao Lyu, Chengqi Zhang, Wenwei Luo, Ping Chen, Kai Shanghai AI Lab Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

One-to-one label assignment in object detection has successfully obviated the need for non-maximum suppression (NMS) as postprocessing and makes the pipeline end-to-end. However, it triggers a new dilemma as the widely used sparse queries cannot guarantee a high recall, while dense queries inevitably bring more similar queries and encounter optimization difficulties. As both sparse and dense queries are problematic, then what are the expected queries in end-to-end object detection? This paper shows that the solution should be Dense Distinct Queries (DDQ). Concretely, we first lay dense queries like traditional detectors and then select distinct ones for one-to-one assignments. DDQ blends the advantages of traditional and recent end-to-end detectors and significantly improves the performance of various detectors including FCN, R-CNN, and DETRs. Most impressively, DDQ-DETR achieves 52.1 AP on MS-COCO dataset within 12 epochs using a ResNet-50 backbone, outperforming all existing detectors in the same setting. DDQ also shares the benefit of end-to-end detectors in crowded scenes and achieves 93.8 AP on Crowd-Human. We hope DDQ can inspire researchers to consider the complementarity between traditional methods and end-to-end detectors. The source code can be found at https://***/jshilong/DDQ.

关键词： detection recognition: Categorization retrieval

来源：评论

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MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action recognition

MITFAS: Mutual Information based Temporal Feature Alignment ...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Xian, Ruiqi Wang, Xijun Manocha, Dinesh Univ Maryland Coll Pk College Pk MD 20742 USA

ISBN: (纸本)9798350318920;9798350318937

We present a novel approach for action recognition in UAV videos. Our formulation is designed to handle occlusion and viewpoint changes caused by the movement of a UAV. We use the concept of mutual information to compute and align the regions corresponding to human action or motion in the temporal domain. This enables our recognition model to learn from the key features associated with the motion. We also propose a novel frame sampling method that uses joint mutual information to acquire the most informative frame sequence in UAV videos. We have integrated our approach with X3D and evaluated the performance on multiple datasets. In practice, we achieve 18.9% improvement in Top-1 accuracy over current state-of-the-art methods on UAV-Human [30], 7.3% improvement on Drone-Action [41], and 7.16% improvement on NEC Drones [7]. The code is available at https://***/Ricky-Xian/MITFAS

关键词： Algorithms Applications Robotics Video recognition and understanding

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Learning to Recognize Occluded and Small Objects with Partial Inputs

Learning to Recognize Occluded and Small Objects with Partia...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zunair, Hasib Ben Hamza, A. Concordia Univ CIISE Montreal PQ Canada

ISBN: (纸本)9798350318920;9798350318937

Recognizing multiple objects in an image is challenging due to occlusions, and becomes even more so when the objects are small. While promising, existing multi-label image recognition models do not explicitly learn context-based representations, and hence struggle to correctly recognize small and occluded objects. Intuitively, recognizing occluded objects requires knowledge of partial input, and hence context. Motivated by this intuition, we propose Masked Supervised Learning (MSL), a single-stage, model-agnostic learning paradigm for multi-label image recognition. The key idea is to learn context-based representations using a masked branch and to model label co-occurrence using label consistency. Experimental results demonstrate the simplicity, applicability and more importantly the competitive performance of MSL against previous state-of-the-art methods on standard multi-label image recognition benchmarks. In addition, we show that MSL is robust to random masking and demonstrate its effectiveness in recognizing non-masked objects. Code and pretrained models are available on GitHub.

关键词： Algorithms Algorithms Algorithms and algorithms Datasets and evaluations formulations Image recognition and understanding Machine learning architectures

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Image Quality-aware Diagnosis via Meta-knowledge Co-embedding

Image Quality-aware Diagnosis via Meta-knowledge Co-embeddin...

引用

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

作者： Che, Haoxuan Chen, Siyu Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge of degradations, models can better resist their adverse effects and avoid misdiagnosis. In this paper, we raise the problem of image quality-aware diagnosis, which aims to take advantage of low-quality images and image quality labels to achieve a more accurate and robust diagnosis. However, the diversity of degradations and superficially unrelated targets between image quality assessment and disease diagnosis makes it still quite challenging to effectively leverage quality labels to assist diagnosis. Thus, to tackle these issues, we propose a novel meta-knowledge co-embedding network, consisting of two subnets: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features, while Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking. Superior performance on five datasets with four widely-used medical imaging modalities demonstrates the effectiveness and generalizability of our method.

关键词： cell microscopy Medical and biological vision

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Toward Stable, Interpretable, and Lightweight Hyperspectral Super-resolution

Toward Stable, Interpretable, and Lightweight Hyperspectral ...

引用

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

作者： Guo, Wen-Jin Xie, Weiying Jiang, Kai Li, Yunsong Lei, Jie Fang, Leyuan Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9798350301298

For real applications, existing HSI-SR methods are not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance caused by the black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior into the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. Based on the spectral mixing prior, we then develop a partial fine-tune strategy to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against the state-of-the-arts under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://***/WenjinGuo/DAEM.

关键词： Low-level vision

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High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

High-Fidelity Zero-Shot Texture Anomaly Localization Using F...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Ardelean, Andrei-Timotei Weyrich, Tim Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9798350318920;9798350318937

We propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. To obtain a high-fidelity localization, we leverage a bijective mapping derived from the 1-dimensional Wasserstein Distance. As opposed to using holistic distances between distributions, the proposed approach allows pinpointing the non-conformity of a pixel in a local context with increased precision. By aggregating the contribution of the pixel to the errors of all nearby patches, we obtain a reliable anomaly score estimate. We validate our solution on several datasets and obtain more than a 40% reduction in error over the previous state of the art on the MVTec AD dataset in a zero-shot setting. Also see ***/pub/***.

关键词： Algorithms Image recognition and understanding

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Prototype-based Embedding Network for Scene Graph Generation

Prototype-based Embedding Network for Scene Graph Generation

引用

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

作者： Zheng, Chaofan Lyu, Xinyu Gao, Lianli Dai, Bo Song, Jingkuan Univ Elect Sci & Technol China Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category, e.g., "man-eating-pizza, giraffe-eating-leaf", and the severe inter-class similarity between different classes, e.g., "man-holding-plate, man-eating-pizza", in model's latent space. The above challenges prevent current SGG methods from acquiring robust features for reliable relation prediction. In this paper, we claim that the predicate's category-inherent semantics can serve as class-wise prototypes in the semantic space for relieving the challenges. To the end, we propose the Prototype-based Embedding Network (PE-Net), which models entities/predicates with prototype-aligned compact and distinctive representations and thereby establishes matching between entity pairs and predicates in a common embedding space for relation recognition. Moreover, Prototype-guided Learning (PL) is introduced to help PE-Net efficiently learn such entity-predicate matching, and Prototype Regularization (PR) is devised to relieve the ambiguous entity-predicate matching caused by the predicate's semantic overlap. Extensive experiments demonstrate that our method gains superior relation recognition capability on SGG, achieving new state-of-the-art performances on both Visual Genome and Open Images datasets. The codes are available at https://***/VL- Group/PENET.

关键词： and reasoning language vision

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Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging

Learning Accurate 3D Shape Based on Stereo Polarimetric Imag...

引用

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

作者： Huang, Tianyu Li, Haoang He, Kejing Sui, Congying Li, Bin Liu, Yun-Hui Chinese Univ Hong Kong Hong Kong Peoples R China Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Shape from Polarization (SfP) aims to recover surface normal using the polarization cues of light. The accuracy of existing SfP methods is affected by two main problems. First, the ambiguity of polarization cues partially results in false normal estimation. Second, the widely-used assumption about orthographic projection is too ideal. To solve these problems, we propose the first approach that combines deep learning and stereo polarization information to recover not only normal but also disparity. Specifically, for the ambiguity problem, we design a Shape Consistency-based Mask Prediction (SCMP) module. It exploits the inherent consistency between normal and disparity to identify the areas with false normal estimation. We replace the unreliable features enclosed by these areas with new features extracted by global attention mechanism. As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy. This strategy is based on the unique pixel-viewing direction encoding, and thus enables our neural network to handle the non-orthographic projection. In addition, we establish a real-world stereo SfP dataset that contains various object categories and illumination conditions. Experiments showed that compared with existing SfP methods, our approach is more accurate. Moreover, our approach shows higher robustness to light variation.

关键词： Physics-based vision and shape-from-X

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InternImage: Exploring Large-Scale vision Foundation Models with Deformable Convolutions

InternImage: Exploring Large-Scale Vision Foundation Models ...

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

作者： Wang, Wenhai Dai, Jifeng Chen, Zhe Huang, Zhenhang Li, Zhiqi Zhu, Xizhou Hu, Xiaowei Lu, Tong Lu, Lewei Li, Hongsheng Wang, Xiaogang Qiao, Yu Shanghai AI Lab Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.

关键词： detection recognition: Categorization retrieval

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