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SpectralTracker: Jointly High and Low-Frequency Modeling for Tracking 6th

SpectralTracker: Jointly High and Low-Frequency Modeling for...

引用

6th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Rong, Yimin Liang, Qihua Li, Ning Mo, Zhiyi Zhong, Bineng Guangxi Normal Univ Minist Educ Key Lab Educ Blockchain & Intelligent Technol Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China

ISBN: (纸本)9789819985548;9789819985555

Recently, a considerable number of top-performing Transformer based trackers have been proposed. However, most of them mainly focus on utilizing low-frequency information from a spatial-spectral analysis perspective, limiting their performance in complicated scenes. To address this problem, we propose a spectral tracker that explores how to capture high and low-frequency information for robust tracking jointly. Specifically, we design a novel dual-spectral information extraction and aggregation module (DSM) consisting of a high and low-frequency branch to capture and combine complementary frequency information of a Transformer effectively. Firstly, we divide the local window in the high-frequency branch to focus on more fine-grained high-frequency information. Then, in the low-frequency branch, we apply AvgPooling with a low-pass effect on a Transformer to amplify its low-frequency information. Furthermore, we design a shared MLP strategy to polarize the dual-frequency branching to high and low-frequency information attention. Finally, we utilize an MLP to complementarily fuse high and low-frequency information for frequency domain modeling. Comprehensive experiments on five tracking benchmarks (i.e., GOT-10k, TrackingNet, LaSOT, UAV123 and TNL2K) show that our spectral tracker achieves better performance than the state-of-the-art trackers.

关键词： Object Tracking vision Transformer Spectral Domain Single-stage Backbone

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Confluent Vessel Trees with Accurate Bifurcations

Confluent Vessel Trees with Accurate Bifurcations

引用

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

作者： Zhang, Zhongwen Marin, Dmitrii Drangova, Maria Boykov, Yuri Univ Waterloo Waterloo ON Canada Vector Res Inst Toronto ON Canada Robarts Res London ON Canada

ISBN: (纸本)9781665445092

We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible. Unsupervised methods can use many structural constraints, e.g. topology, geometry, physics. Common techniques use variants of MST on geodesic tubular graphs minimizing symmetric pairwise costs, i.e. distances. We show limitations of such standard undirected tubular graphs producing typical errors at bifurcations where flow\directedness" is critical. We introduce a new general concept of confluence for continuous oriented curves forming vessel trees and show how to enforce it on discrete tubular graphs. While confluence is a high-order property, we present an efficient practical algorithm for reconstructing confluent vessel trees using minimum arborescence on a directed graph enforcing confluence via simple flow-extrapolating arc construction. Empirical tests on large near-capillary sub-voxel vasculature volumes demonstrate significantly improved reconstruction accuracy at bifurcations. Our code has also been made publicly available(1).

关键词： Geometry computer vision Costs Codes Directed graphs Bifurcation Topology

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Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

Hybrid Rotation Averaging: A Fast and Robust Rotation Averag...

引用

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

作者： Chen, Yu Zhao, Ji Kneip, Laurent Peking Univ Beijing Peoples R China TuSimple San Diego CA USA ShanghaiTech Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

We address rotation averaging (RA) and its application to real-world 3D reconstruction. Local optimisation based approaches are the de facto choice, though they only guarantee a local optimum. Global optimisers ensure global optimality in low noise conditions, but they are inefficient and may easily deviate under the influence of outliers or elevated noise levels. We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method. Combined with a fast view graph filtering as preprocessing, the proposed hybrid approach is robust to outliers. We further apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM), the accuracy and robustness of SfM are both improved by adding the resulting global rotations as regularisers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced.

关键词： Bundle adjustment computer vision Structure from motion Filtering Pipelines Optimization methods Cameras

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Volleyball game tactical data prediction system using computer vision technology 2

Volleyball game tactical data prediction system using comput...

引用

2nd International conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Xie, Jingguang Cheng, Xinnian Guangdong University of Science and Technology Department of Physical Education Dongguan China Jieyang Polytechnic Dept. Arts & Sports Guangdong Jieyang522000 China

ISBN: (纸本)9798350386639

This paper aims to create a highly automated computer vision aided volleyball tactical prediction system, which revolutionizes the flow of data analysis in volleyball games. The system relies on the advanced technology of computer vision and machine learning to extract detailed tactical information directly from the game video stream without manual intervention. This model integrates advanced visual recognition algorithms to generate an adversarial network, which can track player movements, ball trajectories and field layout in real time, and accurately decode the dynamic changes of the game. Using deep neural networks, especially in combination with convolutional neural networks (CNNS) and recurrent neural networks (RNN), the system not only recognizes basic movements, but also analyzes tactical behavior in depth, such as offensive strategy, defensive formation changes, and even predicts the moment of offensive and defensive transition. A key breakthrough is that we have optimized the algorithm to deal with fast movement and occlusion, ensuring high recognition accuracy in complex match situations. Through the introduction of multi-modal fusion and self-attention mechanism, the system can learn and extract rules from massive data, effectively predict the team's tactical preferences and potential scoring opportunities, and provide real-time tactical advice to the coaching team. Experimental verification shows that the system is far superior to the existing solutions in the accuracy of tactical recognition and match result prediction. The application of this model marks a new stage of sports data analysis, brings scientific support for sports optimization, training strategy and competition strategy, and promotes the frontier development of sports science and technology. © 2024 ieee.

关键词： Generative adversarial networks

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SSAN: Separable Self-Attention Network for Video Representation Learning

SSAN: Separable Self-Attention Network for Video Representat...

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

作者： Guo, Xudong Guo, Xun Lu, Yan Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China MSRA Beijing Peoples R China

ISBN: (纸本)9781665445092

Self-attention has been successfully applied to video representation learning due to the effectiveness of modeling long range dependencies. Existing approaches build the dependencies merely by computing the pairwise correlations along spatial and temporal dimensions simultaneously. However;spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning. Intuitively, learning spatial contextual information first will benefit temporal modeling. In this paper;we propose a separable self-attention (SSA) module, which models spatial and temporal correlations sequentially so that spatial contexts can be efficiently used in temporal modeling. By adding SSA module into 21) CNN, we build a SSA network (SSAN) for video representation learning. On the task of video action recognition, our approach outperforms state-of:the-art methods on Something Something and Kinetics-400 datasets. Our models often outperform counterparts with shallower network and fewer modalities. We further verify the semantic learning ability of our method in visual-language task of video retrieval, which showcases the homogeneity of video representations and text embeddings. On MSR-VIT and Youcook2 dowsers, video representations learnt by SSA significantly improve the state-of-the-art performance.

关键词： computer vision Correlation Pairwise error probability Computational modeling Semantics Cognition pattern recognition

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SRKTDN: Applying Super Resolution Method to Dehazing Task

SRKTDN: Applying Super Resolution Method to Dehazing Task

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

作者： Chen, Tianyi Fu, Jiahui Jiang, Wentao Gao, Chen Liu, Si Beihang Univ Shenyuan Honors Coll Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781665448994

Nonhomogeneous haze removal is a challenging problem, which does not follow the physical scattering model of haze. Numerous existing methods focus on homogeneous haze removal by generating transmission map of the image, which is not suitable for nonhomogeneous dehazing tasks. Some methods use end-to-end model but are also designed for homogeneous haze. Inspired by Knowledge Transfer Dehazing Network and Trident Dehazing Network, we propose a model with super-resolution method and knowledge transfer method. Our model consists of a teacher network, a dehaze network and a super-resolution network. The teacher network provides the dehaze network with reliable prior, the dehaze network focuses primarily on haze removal, and the super-resolution network is used to capture details in the hazy image. Ablation study shows that the super-resolution network has significant benefit to image quality. And comparison shows that our model outperforms previous state-of-the-art methods in terms of perceptual quality on NTIRE2021 NonHomogeneous Dehazing Challenge dataset, and also performs well on other datasets.

关键词： Knowledge engineering Image quality Computational modeling conferences Superresolution Scattering pattern recognition

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3D Human Action Representation Learning via Cross-View Consistency Pursuit

3D Human Action Representation Learning via Cross-View Consi...

引用

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

作者： Li, Linguo Wang, Minsi Ni, Bingbing Wang, Hang Yang, Jiancheng Zhang, Wenjun Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multiview complementary supervision signal. CrosSCLR consists of both single-view contrastive learning (Skeleton-CLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner. It is noted that CVC-KM works in such a way that high-confidence positive/negative samples and their distributions are exchanged among views according to their embedding similarity, ensuring cross-view consistency in terms of contrastive context, i.e., similar distributions. Extensive experiments show that CrosSCLR achieves remarkable action recognition results on NTU-60 and NTU-120 dmasets under unsupervised settings, with observed higherquality action representations. Our code is available at https://***/Linguoli/CrosSCLR.

关键词： computer vision Three-dimensional displays Codes Collaborative work pattern recognition

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DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios

DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring ...

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

作者： Bolten, Tobias Pohle-Froehlich, Regina Toennies, Klaus D. Hsch Niederrhein Inst Pattern Recognit Krefeld Germany Univ Magdeburg Dept Simulat & Graph Magdeburg Germany

ISBN: (纸本)9781665448994

Neuromorphic vision sensors are biologically inspired devices which differ fundamentally from well known frame-based sensors. Even though developments in this research area are increasing, applications that rely entirely on event cameras are still relatively rare. This becomes particularly clear when considering real outdoor scenarios apart from laboratory conditions. One obstacle to the development of event-based vision applications in this context may be the lack of labeled datasets for algorithm development and evaluation. Therefore we describe a recording setting of a DVS-based long time monitoring of an urban public area and provide labeled DVS data that also contain effects of environmental outdoor influences recorded in this process. We also describe the processing chain used for label generation, as well as results from a performed denoising benchmark utilizing various spatio-temporal event stream filters. The dataset contains almost 7 hours of real world outdoor event-data with approximate to 47k labeled regions of interest and can be downloaded at http://***/DVS-OUTLAB/

关键词： Privacy Rain Power demand Neuromorphics Noise reduction Pipelines vision sensors

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Line Segment Detection Using Transformers without Edges

Line Segment Detection Using Transformers without Edges

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

作者： Xu, Yifan Xu, Weijian Cheung, David Tu, Zhuowen Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665445092

In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LEM), takes advantages of having integrated tokenized queries, a self-attention mechanism, and encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. In our experiments, we show state-of-the-art results on Wireframe and YorkUrban benchmarks.

关键词： computer vision Casting Image edge detection Computational modeling Detectors Predictive models Transformers

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TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations

TearingNet: Point Cloud Autoencoder to Learn Topology-Friend...

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

作者： Pang, Jiahao Li, Duanshun Tian, Dong InterDigital Princeton NJ 08540 USA

ISBN: (纸本)9781665445092

Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology friendly representations than benchmarks.

关键词： Deep learning Surface reconstruction Network topology Shape Topology pattern recognition Proposals

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