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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

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All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes

All Keypoints You Need: Detecting Arbitrary Keypoints on the...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Katja Ludwig Julian Lorenz Robin Schön Rainer Lienhart Chair for Machine Learning and Computer Vision University of Augsburg

Performance analyses based on videos are commonly used by coaches of athletes in various sports disciplines. In individual sports, these analyses mainly comprise the body posture. This paper focuses on the disciplines of triple, high, and long jump, which require fine-grained locations of the athlete’s body. Typical human pose estimation datasets provide only a very limited set of keypoints, which is not sufficient in this case. Therefore, we propose a method to detect arbitrary keypoints on the whole body of the athlete by leveraging the limited set of annotated keypoints and auto-generated segmentation masks of body parts. Evaluations show that our model is capable of detecting keypoints on the head, torso, hands, feet, arms, and legs, including also bent elbows and knees. We analyze and compare different techniques to encode desired keypoints as the model’s input and their embedding for the Transformer backbone.

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VMCML: Video and Music Matching via Cross-Modality Lifting

VMCML: Video and Music Matching via Cross-Modality Lifting

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Yi-Shan Lee Wei-Cheng Tseng Fu-En Wang Min Sun National Tsing Hua University University of Toronto Vector Institute

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML (Video and Music Matching via Cross-Modality Lifting) that finds a shared embedding space between video and music representations. To ensure the embedding space can be effectively shared by both representations, we leverage CosFace loss based on margin-based cosine similarity loss. Furthermore, to confirm the music is not the original sound of the video and that more than one video is matched to the same music, we follow the rule and collect videos and music from a well-known multi-media platform. That is because there are limitations of previous datasets. We establish a large-scale dataset called MSV, which provide 390 individual music and the corresponding matched 150,000 videos. We conduct extensive experiments on Youtube-8M and our MSV datasets. Our quantitative and qualitative results demonstrate the effectiveness of our proposed framework and achieve state-of-the-art video and music matching performance.

关键词： computer vision conferences Music Recommender systems pattern matching

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The Blessings of Unlabeled Background in Untrimmed Videos

The Blessings of Unlabeled Background in Untrimmed Videos

引用

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

作者： Liu, Yuan Chen, Jingyuan Chen, Zhenfang Deng, Bing Huang, Jianqiang Zhang, Hanwang Alibaba Grp Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as "curses", we consider it as "blessings". Specifically, we first use causal analysis to point out that the common localization errors are due to the unobserved confounder that resides ubiquitously in visual recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA) deconfounder, which exploits the unlabelled background to model an observed substitute for the unobserved confounder, to remove the confounding effect. Note that the proposed deconfounder is model-agnostic and non-intrusive, and hence can be applied in any WTAL method without model re-designs. Through extensive experiments on four state-of-the-art WTAL methods, we show that the deconfounder can improve all of them on the public datasets: THUMOS-14 and ActivityNet-1.3(1).

关键词： Location awareness Training Visualization computer vision Smoothing methods Computational modeling pattern recognition

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Learning Graphs for Knowledge Transfer with Limited Labels

Learning Graphs for Knowledge Transfer with Limited Labels

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

作者： Ghosh, Pallabi Saini, Nirat Davis, Larry S. Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph;for example, the semi-supervised, zero-shot, and few-shot learning setups. We propose a generalized framework for learning and improving the input graph as part of the standard GCN-based learning setup. Moreover, we use additional constraints between similar and dissimilar neighbors for each node in the graph by applying triplet loss on the intermediate layer output. We present results of semi-supervised learning on Citeseer, Cora, and Pubmed benchmarking datasets, and zero/few-shot action recognition on UCF101 and HMDB51 datasets, significantly outperforming current approaches. We also present qualitative results visualizing the graph connections that our approach learns to update.

关键词： Training Visualization computer vision Convolution Semisupervised learning Benchmark testing pattern recognition

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SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning

SelfAugment: Automatic Augmentation Policies for Self-Superv...

引用

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

作者： Reed, Colorado J. Metzger, Sean Srinivas, Aravind Darrell, Trevor Keutzer, Kurt Univ Calif Berkeley BAIR Dept Comp Sci Berkeley CA 94720 USA Weill Neurosci Inst Grad Grp Bioengn Berkeley UCSF San Francisco CA USA UCSF Neurol Surg San Francisco CA USA

ISBN: (纸本)9781665445092

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training process through supervised evaluations is not possible for real-world data that does not actually contain labels (which may be the case, for example, in privacy sensitive fields such as medical imaging). Therefore, in this work we show that evaluating the learned representations with a self-supervised image rotation task is highly correlated with a standard set of supervised evaluations (rank correlation > 0.94). We establish this correlation across hundreds of augmentation policies, training settings, and network architectures and provide an algorithm (SelfAugment) to automatically and efficiently select augmentation policies without using supervised evaluations. Despite not using any labeled data, the learned augmentation policies perform comparably with augmentation policies that were determined using exhaustive supervised evaluations.

关键词： Training computer vision Data privacy Correlation Image recognition Network architecture pattern recognition

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Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To...

引用

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

作者： Changpinyo, Soravit Sharma, Piyush Ding, Nan Soricut, Radu Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [54] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the neaps and Conceptual Captions benchmarks.(1)

关键词： Visualization computer vision Image recognition Pipelines Benchmark testing Data collection Knowledge discovery

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Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors

Fostering Generalization in Single-view 3D Reconstruction by...

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

作者： Bechtold, Jan Tatarchenko, Maxim Fischer, Volker Brox, Thomas Bosch Ctr Artificial Intelligence Renningen Baden Wurttembe Germany Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665445092

Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches predominantly rely on learned global shape priors and, hence, disregard detailed local observations. In this work, we address this issue by learning a hierarchy of priors at different levels of locality from ground truth input depth maps. We argue that exploiting local priors allows our method to efficiently use input observations, thus improving generalization in visible areas of novel shapes. At the same time, the combination of local and global priors enables meaningful hallucination of unobserved parts resulting in consistent 3D shapes. We show that the hierarchical approach generalizes much better than the global approach. It generalizes not only between different instances of a class but also across classes and to unseen arrangements of objects.

关键词： Training computer vision Three-dimensional displays Shape Training data computer architecture Network architecture

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DVMSR: Distillated vision Mamba for Efficient Super-Resolution

DVMSR: Distillated Vision Mamba for Efficient Super-Resoluti...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Xiaoyan Lei Wenlong zhang Weifeng Cao Zhengzhou University of Light Industry The HongKong Polytechnic University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance. Existing state-of-the-art Efficient Image Super-Resolution methods are based on convolutional neural networks. Few attempts have been made with Mamba to harness its long-range modeling capability and efficient computational complexity, which have shown impressive performance on high-level vision tasks. In this paper, we propose DVMSR, a novel lightweight Image SR network that incorporates vision Mamba and a distillation strategy. The network of DVMSR consists of three modules: feature extraction convolution, multiple stacked Residual State Space Blocks (RSSBs), and a reconstruction module. Specifically, the deep feature extraction module is composed of several residual state space blocks (RSSB), each of which has several vision Mamba Moudles(ViMM) together with a residual connection. To achieve efficiency improvement while maintaining comparable performance, we employ a distillation strategy to the vision Mamba network for superior performance. Specifically, we leverage the rich representation knowledge of teacher network as additional supervision for the output of lightweight student networks. Extensive experiments have demonstrated that our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters while maintaining the performance of both PSNR and SSIM. The source code is available at https://***/nathan66666/***

关键词： Knowledge engineering Convolution Computational modeling Source coding Superresolution Feature extraction pattern recognition

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GMOT-40: A Benchmark for Generic Multiple Object Tracking

GMOT-40: A Benchmark for Generic Multiple Object Tracking

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

作者： Bai, Hexin Cheng, Wensheng Chu, Peng Liu, Juehuan Zhang, Kai Ling, Haibin Temple Univ Philadelphia PA 19122 USA SUNY Stony Brook Stony Brook NY 11794 USA Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target (eg, pedestrians), and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little prior information about the target, is largely under-explored. In this paper, we make contributions to boost the study of GMOT in three aspects. First, we construct the first publicly available dense GMOT dataset, dubbed GMOT-40, which contains 40 carefully annotated sequences evenly distributed among 10 object categories. In addition, two tracking protocols are adopted to evaluate different characteristics of tracking algorithms. Second, by noting the lack of devoted tracking algorithms, we have designed a series of baseline GMOT algorithms. Third, we perform a thorough evaluations on GMOT-40, involving popular MOT algorithms (with necessary modifications) and the proposed baselines. The GMOT-40 benchmark is publicly available at https://***/Spritea/GMOT40.

关键词： computer vision Target tracking Protocols Benchmark testing pattern recognition Object tracking

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Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Semi-Supervised Semantic Segmentation with Cross Pseudo Supe...

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

作者： Chen, Xiaokang Yuan, Yuhui Zeng, Gang Wang, Jingdong Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS). Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image. The pseudo one-hot label map, output from one perturbed segmentation network, is used to supervise the other segmentation network with the standard cross-entropy loss, and vice versa. The CPS consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. Experiment results show that our approach achieves the state-of-the-art semi-supervised segmentation performance on Cityscapes and PASCAL VOC 2012.

关键词： Training Image segmentation computer vision Semantics Training data pattern recognition Standards

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