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Sports Field Registration via Keypoints-aware Label Condition

Sports Field Registration via Keypoints-aware Label Conditio...

引用

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

作者： Chu, Yen-Jui Su, Jheng-Wei Hsiao, Kai-Wen Lien, Chi-Yu Fan, Shu-Ho Hu, Min-Chun Lee, Ruen-Rone Yao, Chih-Yuan Chu, Hung-Kuo Natl Tsing Hua Univ Hsinchu Taiwan Delta Res Ctr Hsinchu Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a novel deep learning framework for sports field registration. The typical algorithmic flow for sports field registration involves extracting field-specific features (e.g., corners, lines, etc.) from field image and estimating the homography matrix between a 2D field template and the field image using the extracted features. Unlike previous methods that strive to extract sparse field features from field images with uniform appearance, we tackle the problem differently. First, we use a grid of uniformly distributed keypoints as our field-specific features to increase the likelihood of having sufficient field features under various camera poses. Then we formulate the keypoints detection problem as an instance segmentation with dynamic filter learning. In our model, the convolution filters are generated dynamically, conditioned on the field image and associated keypoint identity, thus improving the robustness of prediction results. To extensively evaluate our method, we introduce a new soccer dataset, called TS-WorldCup, with detailed field markings on 3812 time-sequence images from 43 videos of SoccerWorld Cup 2014 and 2018. The experimental results demonstrate that our method outperforms state-of-the-arts on the TS-WorldCup dataset in both quantitative and qualitative evaluations. Both the code and dataset are available online(1).

关键词： Image segmentation Predictive models Feature extraction Robustness pattern recognition Sparse matrices Task analysis

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FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion vision-Language Pre-training

FashionSAP: Symbols and Attributes Prompt for Fine-grained F...

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

作者： Han, Yunpeng Zhang, Lisai Chen, Qingcai Chen, Zhijian Li, Zhonghua Yang, Jianxin Cao, Zhao Harbin Inst Technol Shenzhen Harbin Peoples R China PengCheng Lab Shenzhen Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Fashion vision-language pre-training models have shown efficacy for a wide range of downstream tasks. However, general vision-language pre-training models pay less attention to fine-grained domain features, while these features are important in distinguishing the specific domain tasks from general tasks. We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics. Firstly, we propose the fashion symbols, a novel abstract fashion concept layer, to represent different fashion items and to generalize various kinds of fine-grained fashion features, making modelling fine-grained attributes more effective. Secondly, the attributes prompt method is proposed to make the model learn specific attributes of fashion items explicitly. We design proper prompt templates according to the format of fashion data. Comprehensive experiments are conducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, and FashionSAP gets SOTA performances for four popular fashion tasks. The ablation study also shows the proposed abstract fashion symbols, and the attribute prompt method enables the model to acquire fine-grained semantics in the fashion domain effectively. The obvious performance gains from FashionSAP provide a new baseline for future fashion task research.

关键词： Multi-modal learning

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Are We Ready for vision-Centric Driving Streaming Perception? The ASAP Benchmark

Are We Ready for Vision-Centric Driving Streaming Perception...

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

作者： Wang, Xiaofeng Zhu, Zheng Zhang, Yunpeng Huang, Guan Ye, Yun Xu, Wenbo Chen, Ziwei Wang, Xingang Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China PhiGent Robot Beijing Peoples R China Southeast Univ Dhaka Bangladesh

ISBN: (纸本)9798350301298

In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal researches and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the offline evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. ASAP project page: https://***/JeffWang987/ASAP.

关键词： Autonomous driving

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Accelerating vision-Language Pretraining with Free Language Modeling

Accelerating Vision-Language Pretraining with Free Language ...

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

作者： Wang, Teng Ge, Yixiao Zheng, Feng Cheng, Ran Shan, Ying Qie, Xiaohu Luo, Ping Southern Univ Sci & Technol Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China ARC Lab Shenzhen Peoples R China Tencent PCG Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corruption rate (percentage of corrupted tokens) in masked language modeling (MLM), that is, a proper corruption rate is achieved at the cost of a large portion of output tokens being excluded from prediction loss. To accelerate the convergence of VLP, we propose a new pretraining task, namely, free language modeling (FLM), that enables a 100% prediction rate with arbitrary corruption rates. FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted. FLM-trained models are encouraged to learn better and faster given the same GPU time by exploiting bidirectional contexts more flexibly. Extensive experiments show FLM could achieve an impressive 2.5x pretraining time reduction in comparison to the MLM-based methods, while keeping competitive performance on both vision-language understanding and generation tasks. Code will be public at https://***/TencentARC/FLM.

关键词： and reasoning language vision

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SwiniPASSR: Swin Transformer based Parallax Attention Network for Stereo Image Super-Resolution

SwiniPASSR: Swin Transformer based Parallax Attention Networ...

引用

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

作者： Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Guo, Guodong Bigo Technol Pte Ltd Beijing Peoples R China Beijing Univ Posts & Telecommun Smart Med Innovat Lab Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With binocular cameras being widely accepted, the study of stereo image super resolution (Stereo SR) has received increasing attention. Different from single image super resolution (SISR) setting, it is more challenging for utilizing both intra-view and cross-view information. Although prior convolution-based works have achieved admirable progress, few attempts have explored the possibility of the Transformer-based architecture for stereo image SR, which has demonstrated promising performance in several visual tasks. In this paper, we propose a novel approach namely SwiniPASSR, which adopts Swin Transformer as the backbone, meanwhile incorporating it with the Bi-directional Parallax Attention Module (biPAM) to maximize auxiliary information given by the binocular mechanism. Even Transformer and parallax attention mechanism (PAM) have been separately proved usefulness by prior studies, we find that simply integrating convolution-based PAM with Transformer or directly optimizing for stereo SR problem was may not achieve desirable result. We therefore introduced a conversion layer to resolve integration and adopted progressive training strategy to learn disparity correspondence through progressively enlarged receptive fields. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed SwiniPASSR. In particular, in the NTIRE 2022: Stereo Image Super-Resolution Challenge, we report 23.71dB PSNR and 0.7295 SSIM performance which ranked 2nd place on the leaderboard.

关键词： Training Convolutional codes Visualization Superresolution Estimation Transformers pattern recognition

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Characterizing Target-absent Human Attention

Characterizing Target-absent Human Attention

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

作者： Chen, Yupei Yang, Zhibo Chakraborty, Souradeep Mondal, Sounak Ahn, Seoyoung Samaras, Dimitris Hoai, Minh Zelinsky, Gregory Smith Kettlewell Eye Res Inst 2232 Webster St San Francisco CA 94115 USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human efficiency in finding a target in an image has attracted the attention of machine learning researchers, but what about when no target is there? Knowing how people search in the absence of a target, and when they stop, is important for Human-computer-interaction systems attempting to predict human gaze behavior in the wild. Here we report a rigorous evaluation of target-absent search behavior using the COCO-Search18 dataset to train state-of-the-art models. We focus on two specific aims. First, we characterize the presence of a target guidance signal in target-absent search behavior by comparing it to target-present guidance and free viewing. We do this by comparing how well a model trained on one type of fixation behavior (target-present, target-absent, free viewing) can predict behavior in either the same or different task. To compare target-absent search to free viewing behavior we created COCO-FreeView, a dataset of free-viewing fixations for the same images used in COCO-Search18. These comparisons revealed the existence of a target guidance signal in target-absent search, albeit one much less dominant compared to when a target actually appeared in an image, and that the target-absent guidance signal was similar to free viewing in that saliency and center bias were both weighted more than guidance from target features. Our second aim focused on the stopping criteria, a question intrinsic to target-absent search. Here we propose to train a foveated target detector whose target detection representation is sensitive to the relationship between distance from the fovea. Then combining the predicted target detection representation with other information such as fixation history and subject ID, our model outperforms the baselines in predicting when a person stops moving his attention during target-absent search.

关键词： computer vision conferences Object detection Machine learning Detectors Predictive models Behavioral sciences

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Investigating Neural Architectures by Synthetic Dataset Design

Investigating Neural Architectures by Synthetic Dataset Desi...

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

作者： Courtois, Adrien Morel, Jean-Michel Arias, Pablo ENS Paris Saclay Ctr Borelli 4 Av Sci F-91190 Gif Sur Yvette France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent years have seen the emergence of many new neural network structures (architectures and layers). To solve a given task, a network requires a certain set of abilities reflected in its structure. The required abilities depend on each task. There is so far no systematic study of the real capacities of the proposed neural structures. The question of what each structure can and cannot achieve is only partially answered by its performance on common benchmarks. Indeed, natural data contain complex unknown statistical cues. It is therefore impossible to know what cues a given neural structure is taking advantage of in such data. In this work, we sketch a methodology to measure the effect of each structure on a network's ability, by designing ad hoc synthetic datasets. Each dataset is tailored to assess a given ability and is reduced to its simplest form: each input contains exactly the amount of information needed to solve the task. We illustrate our methodology by building three datasets to evaluate each of the three following network properties: a) the ability to link local cues to distant inferences, b) the translation covariance and c) the ability to group pixels with the same characteristics and share information among them. Using a first simplified depth estimation dataset, we pinpoint a serious nonlocal deficit of the U-Net. We then evaluate how to resolve this limitation by embedding its structure with nonlocal layers, which allow computing complex features with long-range dependencies. Using a second dataset, we compare different positional encoding methods and use the results to further improve the U-Net on the depth estimation task. The third introduced dataset serves to demonstrate the need for self-attention-like mechanisms for resolving more realistic depth estimation tasks.

关键词： computer vision Systematics conferences Neural networks Buildings Estimation computer architecture

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Collaborative Static and Dynamic vision-Language Streams for Spatio-Temporal Video Grounding

Collaborative Static and Dynamic Vision-Language Streams for...

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

作者： Lin, Zihang Tan, Chaolei Hu, Jian-Fang Jin, Zhi Ye, Tiancai Zheng, Wei-Shi Sun Yat Sen Univ Guangzhou Peoples R China Tencent Shenzhen Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatio-temporal visual-linguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intra-frame visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall framework achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets.

关键词： language reasoning vision

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Teaching Structured vision & Language Concepts to vision & Language Models

Teaching Structured Vision & Language Concepts to Vision & L...

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

作者： Doveh, Sivan Arbelle, Assaf Harary, Sivan Schwartz, Eli Herzig, Roei Giryes, Raja Feris, Rogerio Panda, Rameswar Ullman, Shimon Karlinsky, Leonid IBM Res Yorktown Hts NY 10598 USA Weizmann Inst Sci Rehovot Israel Tel Aviv Univ Tel Aviv Israel MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a challenge. We introduce the collective notion of Structured vision & Language Concepts (SVLC) which includes object attributes, relations, and states which are present in the text and visible in the image. Recent studies have shown that even the best VL models struggle with SVLC. A possible way of fixing this issue is by collecting dedicated datasets for teaching each SVLC type, yet this might be expensive and time-consuming. Instead, we propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs that makes more effective use of existing VL pre-training datasets and does not require any additional data. While automatic understanding of image structure still remains largely unsolved, language structure is much better modeled and understood, allowing for its effective utilization in teaching VL models. In this paper, we propose various techniques based on language structure understanding that can be used to manipulate the textual part of off-the-shelf paired VL datasets. VL models trained with the updated data exhibit a significant improvement of up to 15% in their SVLC understanding with only a mild degradation in their zero-shot capabilities both when training from scratch or fine-tuning a pre-trained model. Our code and pretrained models are available at: https://***/SivanDoveh/TSVLC

关键词： Multi-modal learning

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Uncertainty-aware vision-based Metric Cross-view Geolocalization

Uncertainty-aware Vision-based Metric Cross-view Geolocaliza...

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

作者： Fervers, Florian Bullinger, Sebastian Bodensteiner, Christoph Arens, Michael Stiefelhagen, Rainer Fraunhofer IOSB Ettlingen Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798350301298

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous driving, i.e. using expensive high-definition prior maps or relying entirely on the sensor data captured at runtime. We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We combine multiple vehicle datasets with aerial images from orthophoto providers on which we demonstrate the feasibility of our method. Since the ground truth poses are often inaccurate w.r.t. the aerial images, we implement a pseudo-label approach to produce more accurate ground truth poses and make them publicly available. While previous works require training data from the target region to achieve reasonable localization accuracy (i.e. same-area evaluation), our approach overcomes this limitation and outperforms previous results even in the strictly more challenging cross-area case. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region, which highlights the model's potential for global-scale application. We further integrate the uncertainty-aware predictions in a tracking framework to determine the vehicle's trajectory over time resulting in a mean position error on KITTI-360 of 0.78m.

关键词： Autonomous driving

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