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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

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

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

ISBN: (纸本)9798350302493

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. © 2023 ieee.

关键词： Sports

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CLIP-Guided vision-Language Pre-training for Question Answering in 3D Scenes

CLIP-Guided Vision-Language Pre-training for Question Answer...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Parelli, Maria Delitzas, Alexandros Hars, Nikolas Vlassis, Georgios Anagnostidis, Sotirios Bachmann, Gregor Hofmann, Thomas Eth Zurich Switzerland

ISBN: (纸本)9798350302493

Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to explore. In this work, we design a novel 3D pre-training vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representations. We inject the representational power of the popular CLIP model into our 3D encoder by aligning the encoded 3D scene features with the corresponding 2D image and text embeddings produced by CLIP. To assess our model's 3D world reasoning capability, we evaluate it on the downstream task of 3D Visual Question Answering. Experimental quantitative and qualitative results show that our pre-training method outperforms state-of-the-art works in this task and leads to an interpretable representation of 3D scene features. © 2023 ieee.

关键词： computer vision

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Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

Theoretically Achieving Continuous Representation of Oriente...

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

作者： Xiao, Zi-Kai Yang, Guo-Ye Yang, Xue Mul, Tai-Jiang Yan, Junchi Hui, Shi-Min Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab AI Shanghai Peoples R China

ISBN: (纸本)9798350353006

Considerable efforts have been devoted to Oriented Object Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) representation remains unresolved, which is an inherent bottleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this direction. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio, and often inadvertently introduce decoding discontinuity, e.g. Decoding Incompleteness (DI) and Decoding Ambiguity (DA) as discussed in literature. Specifically, we propose a novel representation method called Continuous OBB (COBB), which can be readily integrated into existing detectors e.g. Faster-RCNN as a plugin. It can theoretically ensure continuity in bounding box regression which to our best knowledge, has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments, we have developed a modularized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDet for OOD evaluation. On the popular DOTA dataset, by integrating Faster-RCNN as the same baseline model, our new method outperforms the peer method Gliding Vertex by 1.13% mAP(50) (relative improvement 1.54%), and 2.46% mAP(75) (relative improvement 5.91%), without any tricks.

关键词： boundary problem computer vision object detection oriented object detection remote sensing

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SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity recognition

SPARTAN: Self-supervised Spatiotemporal Transformers Approac...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Chappa, Naga V. S. Raviteja Nguyen, Pha Nelson, Alexander H. Seo, Han-Seok Li, Xin Dobbs, Page Daniel Luu, Khoa University of Arkansas United States West Virginia University United States

ISBN: (纸本)9798350302493

In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-supervised objective aims to match the features of these contrasting views representing the same video to be consistent with the variations in spatiotemporal domains. To the best of our knowledge, the proposed mechanism is one of the first works to alleviate the weakly supervised setting of GAR using the encoders in video transformers. Furthermore, using the advantage of transformer models, our proposed approach supports long-term relationship modeling along spatio-temporal dimensions. The proposed SPARTAN approach performs well on two group activity recognition benchmarks, including NBA and Volleyball datasets, by surpassing the state-of-the-art results by a significant margin in terms of MCA and MPCA metrics1. © 2023 ieee.

关键词： computer vision

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GP22: A Car Styling Dataset for Automotive Designers

GP22: A Car Styling Dataset for Automotive Designers

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

作者： Lee, Gyunpyo Kim, Taesu Suk, Hyeon-Jeong Korea Adv Inst Sci & Technol Dept Ind Design Color Lab Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

An automated design data archiving could reduce the time wasted by designers from working creatively and effectively. Though many datasets on classifying, detecting, and instance segmenting on car exterior exist, these large datasets are not relevant for design practices as the primary purpose lies in autonomous driving or vehicle verification. Therefore, we release GP22, composed of car styling features defined by automotive designers. The dataset contains 1480 car side profile images from 37 brands and ten car segments. It also contains annotations of design features that follow the taxonomy of the car exterior design features defined in the eye of the automotive designer. We trained the baseline model using YOLO v5 as the design feature detection model with the dataset. The presented model resulted in an mAP score of 0.995 and a recall of 0.984. Furthermore, exploration of the model performance on sketches and rendering images of the car side profile implies the scalability of the dataset for design purposes.

关键词： Image segmentation Annotations Feature detection Scalability Taxonomy Rendering (computer graphics) pattern recognition

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Efficient Conditional Pre-training for Transfer Learning

Efficient Conditional Pre-training for Transfer Learning

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

作者： Chakraborty, Shuvam Uzkent, Burak Ayush, Kumar Tanmay, Kumar Sheehan, Evan Ermon, Stefano Stanford Univ Stanford CA 94305 USA IIT Kharagpur Kharagpur W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This strategy helps reduce dependence on the target dataset and improves convergence rate and generalization on the target task. Although pre-training on large-scale datasets is very useful for new methods or models, its foremost disadvantage is high training cost. To address this, we propose efficient filtering methods to select relevant subsets from the pre-training dataset. Additionally, we discover that lowering image resolutions in the pre-training step offers a great trade-off between cost and performance. We validate our techniques by pre-training on ImageNet in both the unsupervised and supervised settings and finetuning on a diverse collection of target datasets and tasks. Our proposed methods drastically reduce pre-training cost and provide strong performance boosts. Finally, we improve the current standard of ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.

关键词： Training computer vision Costs Image resolution Filtering Computational modeling Transfer learning

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

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state of the art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which only the image is input, without any text. Our code is available at https://***/talshaharabany/Similarity-Maps-forSelf-Training-Weakly-Supervised- Phrase-Grounding.

关键词： language reasoning vision

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Concept Activation Vectors for Generating User-Defined 3D Shapes

Concept Activation Vectors for Generating User-Defined 3D Sh...

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

作者： Druc, Stefan Balu, Aditya Wooldridge, Peter Krishnamurthy, Adarsh Sarkar, Soumik MonolithAI 12-18 Hoxton St London N1 6NG England Iowa State Univ Ames IA 50011 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We explore the interpretability of 3D geometric deep learning models in the context of computer-Aided Design (CAD). The field of parametric CAD can be limited by the difficulty of expressing high-level design concepts in terms of a few numeric parameters. In this paper, we use a deep learning architectures to encode high dimensional 3D shapes into a vectorized latent representation that can be used to describe arbitrary concepts. Specifically, we train a simple auto-encoder to parameterize a dataset of complex shapes. To understand the latent encoded space, we use the idea of Concept Activation Vectors (CAV) to reinterpret the latent space in terms of user-defined concepts. This allows modification of a reference design to exhibit more or fewer characteristics of a chosen concept or group of concepts. We also test the statistical significance of the identified concepts and determine the sensitivity of a physical quantity of interest across the dataset.

关键词： Deep learning Solid modeling Three-dimensional displays Design automation Sensitivity Shape pattern recognition

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Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets

Delving into High-Quality Synthetic Face Occlusion Segmentat...

引用

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

作者： Voo, Kenny T. R. Jiang, Liming Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications. The collection and annotation of such datasets are time-consuming and labor-intensive. Although some efforts have been made in synthetic data generation, the naturalistic aspect of data remains less explored. In our study, we propose two occlusion generation techniques, Naturalistic Occlusion Generation (NatOcc), for producing high-quality naturalistic synthetic occluded faces;and Random Occlusion Generation (RandOcc), a more general synthetic occluded data generation method (Figure 1). We empirically show the effectiveness and robustness of both methods, even for unseen occlusions. To facilitate model evaluation, we present two high-resolution real-world occluded face datasets with fine-grained annotations, RealOcc and RealOcc-Wild, featuring both careful alignment preprocessing and an in-the-wild setting for robustness test. We further conduct a comprehensive analysis on a newly introduced segmentation benchmark, offering insights for future exploration. Our code and dataset are available at https://***/kennyvoo/faceocclusion-generation.

关键词： computer vision Codes Annotations Face recognition conferences Benchmark testing Robustness

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A Two-Stage Shake-Shake Network for Long-Tailed recognition of SAR Aerial View Objects

A Two-Stage Shake-Shake Network for Long-Tailed Recognition ...

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

作者： Li, Gongzhe Pan, Linpeng Qiu, Linwei Tan, Zhiwen Xie, Fengying Zhang, Haopeng Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) has received more attention due to its complementary superiority on capturing significant information in the remote sensing area. However, for an Aerial View Object Classification (AVOC) task, SAR images still suffer from the long-tailed distribution of the aerial view objects. This disparity limit the performance of classification methods, especially for the data-sensitive deep learning models. In this paper, we propose a two-stage shake-shake network to tackle the long-tailed learning problem. Specifically, it decouples the learning procedure into the representation learning stage and the classification learning stage. Moreover, we apply the test time augmentation (TTA) and the classification with alternating normalization (CAN) to improve the accuracy. In the PBVS 1 2022 Multi-modal Aerial View Object Classification Challenge Track 1, our method achieves 21.82% and 27.97% accuracy in the development phase and testing phase respectively, which wins the top-tier among all the participants.

关键词： Training Representation learning Feature extraction Radar tracking Radar polarimetry pattern recognition Task analysis

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