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MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving

MV-TAL: Mulit-view Temporal Action Localization in Naturalis...

引用

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

作者： Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in naturalistic driving. Specifically, we adopted SwinTransformer as feature extractor, and a single framework to detect boundary and class at the same time. Also, we improve multiple loss function for explicit constraints of embedded feature distributions. Our proposed framework achieves the overall F1 -score of 0.3154 on A2 dataset.

关键词： Location awareness Measurement Visualization computer vision conferences Aggregates Gray-scale

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Smoothing-based optimization

Smoothing-based optimization

引用

ieee conference on computer vision and pattern recognition

作者： Leordeanu, Marius Hebert, Martial Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781424422425

We propose an efficient method for complex optimization problems that often arise in computer vision. While our method is general and could be applied to various tasks, it was mainly inspired from problems in computer vision, and it borrows ideas from scale space theory. One of the main motivations for our approach is that searching for the global maximum through the scale space of a function is equivalent to looking for the maximum of the original function, with the advantage of having to handle fewer local optima. Our method works with any non-negative, possibly non-smooth function, and requires only the ability of evaluating the function at any specific point. The algorithm is based on a growth transformation, which is guaranteed to increase the value of the scale space function at every step, unlike gradient methods. To demonstrate its effectiveness we present its performance on a few computer vision applications, and show that in our experiments it is more effective than some well established methods such as MCMC, Simulated Annealing and the more local Nelder-Mead optimization method.

关键词： computer vision

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Convexity and Bayesian Constrained Local Models

Convexity and Bayesian Constrained Local Models

引用

ieee-computer-Society conference on computer vision and pattern recognition Workshops

作者： Paquet, Ulrich Imense Ltd Cambridge England

ISBN: (纸本)9781424439928

The accurate localization of facial features plays a fundamental role in any face recognition pipeline. Constrained local models (CLM) provide an effective approach to localization by coupling ensembles of local patch detectors for non-rigid object alignment. A recent improvement has been made by using generic convex quadratic fitting (CQF), which elegantly addresses the CLM warp update by enforcing convexity of the patch response surfaces. In this paper, CQF is generalized to a Bayesian inference problem, in which it appears as a particular maximum likelihood solution. The Bayesian viewpoint holds many advantages: for example, the task of feature localization can explicitly build on previous face detection stages, and multiple sets of patch responses can be seamlessly incorporated. A second contribution of the paper is an analytic solution to finding convex approximations to patch response surfaces, which removes CQF's reliance on a numeric optimizer Improvements in feature localization performance are illustrated on the Labeled Faces in the Wild and BioID data sets.

关键词： Face recognition

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An In-depth Exploration of Person Re-identification and Gait recognition in Cloth-Changing Conditions

An In-depth Exploration of Person Re-identification and Gait...

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

作者： Li, Weijia Hou, Saihui Zhang, Chunjie Cao, Chunshui Liu, Xu Huang, Yongzhen Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China WATRIX AI Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (纸本)9798350301298

The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions. To tackle this problem, we propose a Cloth-Changing benchmark for Person re-identification and Gait recognition (CCPG). It is a cloth-changing dataset, and there are several highlights in CCPG, (1) it provides 200 identities and over 16K sequences are captured indoors and outdoors, (2) each identity has seven different cloth-changing statuses, which is hardly seen in previous datasets, (3) RGB and silhouettes version data are both available for research purposes. Moreover, aiming to investigate the cloth-changing problem systematically, comprehensive experiments are conducted on video-based ReID and gait recognition methods. The experimental results demonstrate the superiority of ReID and gait recognition separately in different cloth-changing conditions and suggest that gait recognition is a potential solution for addressing the cloth-changing problem. Our dataset will be available at https://***/BNU-IVC/CCPG.

关键词： Datasets and evaluation

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Real-time gesture recognition with minimal training requirements and on-line learning

Real-time gesture recognition with minimal training requirem...

引用

ieee conference on computer vision and pattern recognition

作者： Rajko, Stjepan Qian, Gang Ingalls, Todd James, Jodi Arizona State Univ Arts Media & Engn Program Tempe AZ 85287 USA

ISBN: (纸本)9781424411795

In this paper, we introduce the semantic network model (SNM), a generalization of the hidden Markov model (HMM) that uses factorization of state transition probabilities to reduce training requirements, increase the efficiency of gesture recognition and on-line learning, and allow more precision in gesture modeling. We demonstrate the advantages both formally and experimentally, using examples such as full-body multimodal gesture recognition via optical motion capture and a pressure sensitive floor as well as mouse / pen gesture recognition. Our results show that our algorithm performs much better than the traditional approach in situations where training samples are limited and/or the precision of the gesture model is high.

关键词： Gesture recognition

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MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

MetaCorrection: Domain-aware Meta Loss Correction for Unsupe...

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

作者： Guo, Xiaoqing Yang, Chen Li, Baopu Yuan, Yixuan City Univ Hong Kong Hong Kong Peoples R China Baidu USA Sunnyvale CA USA

ISBN: (纸本)9781665445092

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation. However;the generated pseudo labels from the model optimized on the source domain inevitably contain noise due to the domain gap. To tackle this issue, we advance a MetaCorrection framework, where a Domain-aware Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA semantic segmentation. In particular, we model the noise distribution of pseudo labels in target domain by introducing a noise transition matrix (NTM) and construct meta data set with domain-invariant source data to guide the estimation of NTM. Through the risk minimization on the meta data set, the optimized NTM thus can correct the noisy issues in pseudo labels and enhance the generalization ability of the model on the target data. Considering the capacity gap between shallow and deep features, we fitrther employ the proposed DMLC strategy to provide matched and compatible supervision signals for different level features, thereby ensuring deep adaptation. Extensive experimental results highlight the effectiveness of our method' against existing state-of-the-art methods on three benchmarks.

关键词： Bridges Adaptation models computer vision Computational modeling Semantics Estimation Metadata

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Online Multiple Object Tracking with Cross-Task Synergy

Online Multiple Object Tracking with Cross-Task Synergy

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

作者： Guo, Song Wang, Jingya Wang, Xinchao Tao, Dacheng Univ Sydney Sydney NSW Australia ShanghaiTech Univ Shanghai Peoples R China Natl Univ Singapore Singapore Singapore Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781665445092

Modern online multiple object tracking (MOT) methods usually focus on two directions to improve tracking performance. One is to predict new positions in an incoming frame based on tracking information from previous frames, and the other is to enhance data association by generating more discriminative identity embeddings. Some works combined both directions within one framework but handled them as two individual tasks, thus gaining little mutual benefits. In this paper, we propose a novel unified model with synergy between position prediction and embedding association. The two tasks are linked by temporal-aware target attention and distractor attention, as well as identity-aware memory aggregation model. Specifically, the attention modules can make the prediction focus more on targets and less on distractors, therefore more reliable embeddings can be extracted accordingly for association. On the other hand, such reliable embeddings can boost identity-awareness through memory aggregation, hence strengthen attention modules and suppress drifts. In this way, the synergy between position prediction and embedding association is achieved, which leads to strong robustness to occlusions. Extensive experiments demonstrate the superiority of our proposed model over a wide range of existing methods on MOTChallenge benchmarks. Our code and models are publicly available at https://***/songguocod/TADAM.

关键词： computer vision Codes Interference Predictive models Benchmark testing Reliability engineering Robustness

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End-to-End Multi-Person Pose Estimation with Transformers

End-to-End Multi-Person Pose Estimation with Transformers

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

作者： Shi, Dahu Wei, Xing Li, Liangqi Ren, Ye Tan, Wenming Hikvis Res Inst Hangzhou Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current methods of multi-person pose estimation typically treat the localization and association of body joints separately. In this paper, we propose the first filly end-to-end multi-person Pose Estimation framework with TRansformers, termed PETR. Our method views pose estimation as a hierarchical set prediction problem and effectively removes the need for many hand-crafted modules like Rol cropping, NMS and grouping post-processing. In PETR, multiple pose queries are learned to directly reason a set offull-body poses. Then a joint decoder is utilized to further refine the poses by exploring the kinematic relations between body joints. With the attention mechanism, the proposed method is able to adaptively attend to the features most relevant to target keypoints, which largely overcomes the feature misalignment difficulty in pose estimation and improves the performance considerably. Extensive experiments on the MS COCO and CrowdPose benchmarks show that PETR plays favorably against state-of-the-art approaches in terms of both accuracy and efficiency. The code and models are available at https://***/hikvision-research/opera.

关键词： Location awareness computer vision Image analysis Codes Pose estimation Kinematics Benchmark testing

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Capturing Omni-Range Context for Omnidirectional Segmentation

Capturing Omni-Range Context for Omnidirectional Segmentatio...

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

作者： Yang, Kailun Zhang, Jiaming Reiss, Simon Hu, Xinxin Stiefelhagen, Rainer Karlsruhe Inst Technol CV HCI Lab Karlsruhe Germany Carl Zeiss Jena Germany Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street-scenes, omnidirectional cameras present themselves as a perfect fit in such systems. Most segmentation models for parsing urban environments operate on common, narrow Field of View (FoV) images. Transferring these models from the domain they were designed for to 360. perception, their performance drops dramatically, e.g., by an absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of FoV and structural distribution between the imaging domains, we introduce Efficient Concurrent Attention Networks (ECANets), directly capturing the inherent long-range dependencies in omnidirectional imagery. In addition to the learned attention-based contextual priors that can stretch across 360. images, we upgrade model training by leveraging multi-source and omni-supervised learning, taking advantage of both: Densely labeled and unlabeled data originating from multiple datasets. To foster progress in panoramic image segmentation, we put forward and extensively evaluate models on Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture diverse scenes from all around the globe. Our novel model, training regimen and multi-source prediction fusion elevate the performance (mIoU) to new state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS (69.0%) benchmarks.(1)

关键词： Training Image segmentation computer vision Computational modeling Semantics Urban areas Predictive models

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Brain-inspired Multilayer Perceptron with Spiking Neurons

Brain-inspired Multilayer Perceptron with Spiking Neurons

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

作者： Li, Wenshuo Chen, Hanting Guo, Jianyuan Zhang, Ziyang Wang, Yunhe Huawei Noahs Ark Lab Hong Kong Peoples R China Peking Univ Dept Machine Intelligence Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without inductive bias, MLPs perform well on feature extraction and achieve amazing results. However, due to the simplicity of their structures, the performance highly depends on the local features communication machenism. To further improve the performance of MLP, we introduce information communication mechanisms from brain-inspired neural networks. Spiking Neural Network (SNN) is the most famous brain-inspired neural network, and achieve great success on dealing with sparse data. Leaky Integrate and Fire (LIF) neurons in SNNs are used to communicate between different time steps. In this paper, we incorporate the machanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNN-MLP model achieves 81.9%, 83.3% and 83.5% topI accuracy on ImageNet dataset with only 4.4G, 8.5G and I 5.2G FLOPs, respectively, which are state-of-the-art results as far as we know The source code will be available at https://***/mindspore/models/tree/master/research/cv/snn_mlp.

关键词： computer vision Computational modeling Neurons Multilayer perceptrons Feature extraction Brain modeling Transformers

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