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

共 22906 条记录，以下是4841-4850 订阅

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Real-time detection and understanding of isolated protruded fingers

Real-time detection and understanding of isolated protruded ...

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, CVPRW 2004

作者： Chandran, Sharat Sawa, Abhineet Computer Science and Engg. Dept. IIT Bombay and CfAR Univ. of Maryland Mumbai400076 India Computer Science and Engg. Dept. IIT Bombay Mumbai400076 India

Making sense of hand gestures in applications such as novel user interfaces is an active area of research. Many current methods use complex 3D model of non-rigid hands and involved paradigms in solving the general problem. In this paper we look at a subproblem of sign language alphabet recognition where gestures are made with protruded fingers. In contrast to the more intricate schemes, we propose a simple, fast classification algorithm using two dimensional projection of Euler angles. Not only is our scheme novel, it lends itself to a real time implementation. © 2004 ieee.

关键词： User interfaces

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Deep Generalized Unfolding Networks for Image Restoration

Deep Generalized Unfolding Networks for Image Restoration

引用

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

作者： Mou, Chong Wang, Qian Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep neural networks (DNN) have achieved great success in image restoration. However, most DNN methods are designed as a black box, lacking transparency and interpretability. Although some methods are proposed to combine traditional optimization algorithms with DNN, they usually demand pre-defined degradation processes or handcrafted assumptions, making it difficult to deal with complex and real-world applications. In this paper, we propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation. In addition, we design inter-stage information pathways across proximal mapping in different PGD iterations to rectify the intrinsic information loss in most deep unfolding networks (DUN) through a multi-scale and spatial-adaptive way. By integrating the flexible gradient descent and informative proximal mapping, we unfold the iterative PGD algorithm into a trainable DNN. Extensive experiments on various image restoration tasks demonstrate the superiority of our method in terms of state-of-theart performance, interpretability, and generalizability.

关键词： Degradation Deep learning computer vision Neural networks Estimation Iterative algorithms Image restoration

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COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

COOPERNAUT: End-to-End Driving with Cooperative Perception f...

引用

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

作者： Cui, Jiaxun Qiu, Hang Chen, Dian Stone, Peter Zhu, Yuke Univ Texas Austin Austin TX 78712 USA Stanford Univ Stanford CA 94305 USA Sony AI Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model encodes LiDAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels. To evaluate our model, we develop AUTOCASTSIM, a network-augmented driving simulation framework with example accident-prone scenarios. Our experiments on AUTOCASTSIM suggest that our cooperative perception driving models lead to a 40% improvement in average success rate over egocentric driving models in these challenging driving situations and a 5x smaller bandwidth requirement than prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at https://***/Coopernaut/.

关键词： Wireless communication Wireless sensor networks computer vision Ultraviolet sources Computational modeling Vehicular ad hoc networks pattern recognition

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Ranking Distance Calibration for Cross-Domain Few-Shot Learning

Ranking Distance Calibration for Cross-Domain Few-Shot Learn...

引用

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

作者： Li, Pan Gong, Shaogang Wang, Chengjie Fu, Yanwei Queen Mary Univ London London England Tencent Youtu Lab Shenzhen Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are in different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more information in the target domain rather than to overly elaborate training strategies on the source domain as in many existing methods. Hence, we start from a generic representation pre-trained by a cross-entropy loss and a conventional distance-based classifier, along with an image retrieval view, to employ a re-ranking process to calibrate a target distance matrix by discovering the k-reciprocal neighbours within the task. Assuming the pre-trained representation is biased towards the source, we construct a non-linear subspace to minimise task-irrelevant features there within while keep more transferrable discriminative information by a hyperbolic tangent transformation. The calibrated distance in this target-aware non-linear subspace is complementary to that in the pre-trained representation. To impose such distance calibration information onto the pre-trained representation, a Kullback-Leibler divergence loss is employed to gradually guide the model towards the calibrated distance-based distribution. Extensive evaluations on eight target domains show that this target ranking calibration process can improve conventional distance-based classifiers in few-shot learning.

关键词： Training computer vision Image retrieval Encoding Calibration pattern recognition Task analysis

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Multi-Level Representation Learning with Semantic Alignment for Referring Video Object Segmentation

Multi-Level Representation Learning with Semantic Alignment ...

引用

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

作者： Wu, Dongming Dong, Xingping Shao, Ling Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Terminus Grp Beijing Peoples R China Univ Macau SKL IOTSC Zhuhai Peoples R China IIAI Abu Dhabi U Arab Emirates

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Referring video object segmentation (RVOS) is a challenging language-guided video grounding task, which requires comprehensively understanding the semantic information of both video content and language queries for object prediction. However, existing methods adopt multi-modal fusion at a frame-based spatial granularity. The limitation of visual representation is prone to causing vision-language mismatching and producing poor segmentation results. To address this, we propose a novel multi-level representation learning approach, which explores the inherent structure of the video content to provide a set of discriminative visual embedding, enabling more effective vision-language semantic alignment. Specifically, we embed different visual cues in terms of visual granularity, including multi-frame long-temporal information at video level, intra-frame spatial semantics at frame level, and enhanced object-aware feature prior at object level. With the powerful multi-level visual embedding and carefully-designed dynamic alignment, our model can generate a robust representation for accurate video object segmentation. Extensive experiments on Refer-DAVIS 17 and Refer-YouTube-VOS demonstrate that our model achieves superior performance both in segmentation accuracy and inference speed.

关键词： Representation learning Visualization computer vision Adaptation models Shape Grounding Semantics

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recognition with local features: the kernel recipe

Recognition with local features: the kernel recipe

引用

9th ieee International conference on computer vision

作者： Wallraven, C Caputo, B Graf, A MPI Biol Cybernet Tubingen Germany

ISBN: (纸本)0769519504

Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper we combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that it is suitable for many established local feature frameworks. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs with the proposed kernel perform better than standard matching techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.

关键词： computer vision

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Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization

Coming Down to Earth: Satellite-to-Street View Synthesis for...

引用

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

作者： Toker, Aysim Zhou, Qunjie Maximov, Maxim Leal-Taixe, Laura Tech Univ Munich Munich Germany

ISBN: (纸本)9781665445092

The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. This task is notoriously challenging due to the drastic viewpoint and appearance differences between the two domains. We show that we can address this discrepancy explicitly by learning to synthesize realistic street views from satellite inputs. Following this observation, we propose a novel multi-task architecture in which image synthesis and retrieval are considered jointly. The rationale behind this is that we can bias our network to learn latent feature representations that are useful for retrieval if we utilize them to generate images across the two input domains. To the best of our knowledge, ours is the first approach that creates realistic street views from satellite images and localizes the corresponding query street-view simultaneously in an end-to-end manner. In our experiments, we obtain state-of-the-art performance on the CVUSA and CVACT benchmarks. Finally, we show compelling qualitative results for satellite-to-street view synthesis.

关键词： Earth Location awareness Satellites Image synthesis Semantics Image retrieval computer architecture

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Improving Adversarially Robust Few-shot Image Classification with Generalizable Representations

Improving Adversarially Robust Few-shot Image Classification...

引用

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

作者： Dong, Junhao Wang, Yuan Lai, Jianhuang Xie, Xiaohua Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-Shot Image Classification (FSIC) aims to recognize novel image classes with limited data, which is significant in practice. In this paper, we consider the FSIC problem in the case of adversarial examples. This is an extremely challenging issue because current deep learning methods are still vulnerable when handling adversarial examples, even with massive labeled training samples. For this problem, existing works focus on training a network in the metalearning fashion that depends on numerous sampled fewshot tasks. In comparison, we propose a simple but effective baseline through directly learning generalizable representations without tedious task sampling, which is robust to unforeseen adversarial FSIC tasks. Specifically, we introduce an adversarial-aware mechanism to establish auxiliary supervision via feature-level differences between legitimate and adversarial examples. Furthermore, we design a novel adversarial-reweighted training manner to alleviate the imbalance among adversarial examples. The feature purifier is also employed as post-processing for adversarial features. Moreover, our method can obtain generalizable representations to remain superior transferability, even facing cross-domain adversarial examples. Extensive experiments show that our method can significantly outperform state-of-the-art adversarially robust FSIC methods on two standard benchmarks.

关键词： Training Deep learning computer vision Image recognition Benchmark testing pattern recognition Task analysis

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Continual Stereo Matching of Continuous Driving Scenes with Growing Architecture

Continual Stereo Matching of Continuous Driving Scenes with ...

引用

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

作者： Zhang, Chenghao Tian, Kun Fan, Bin Meng, Gaofeng Zhang, Zhaoxiang Pan, Chunhong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China HK Inst Sci & Innovat CAS Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The deep stereo models have achieved state-of-the-art performance on driving scenes, but they suffer from severe performance degradation when tested on unseen scenes. Although recent work has narrowed this performance gap through continuous online adaptation, this setup requires continuous gradient updates at inference and can hardly deal with rapidly changing scenes. To address these challenges, we propose to perform continual stereo matching where a model is tasked to 1) continually learn new scenes, 2) overcome forgetting previously learned scenes, and 3) continuously predict disparities at deployment. We achieve this goal by introducing a Reusable Architecture Growth (RAG) framework. RAG leverages task-specific neural unit search and architecture growth for continual learning of new scenes. During growth, it can maintain high reusability by reusing previous neural units while achieving good performance. A module named Scene Router is further introduced to adaptively select the scene-specific architecture path at inference. Experimental results demonstrate that our method achieves compelling performance in various types of challenging driving scenes.

关键词： Degradation Adaptation models computer vision computer architecture Predictive models Task analysis pattern matching

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Human Action recognition Based on Context-Dependent Graph Kernels 27

Human Action Recognition Based on Context-Dependent Graph Ke...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Wu, Baoxin Yuan, Chunfeng Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781479951178

Graphs are a powerful tool to model structured objects, but it is nontrivial to measure the similarity between two graphs. In this paper, we construct a two-graph model to represent human actions by recording the spatial and temporal relationships among local features. We also propose a novel family of context-dependent graph kernels (CGKs) to measure similarity between graphs. First, local features are used as the vertices of the two-graph model and the relationships among local features in the intra-frames and inter-frames are characterized by the edges. Then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model. Graphs can be decomposed into numbers of primary walk groups with different walk lengths and our CGKs are based on the context-dependent primary walk group matching. Taking advantage of the context information makes the correctly matched primary walk groups dominate in the CGKs and improves the performance of similarity measurement between graphs. Finally, a generalized multiple kernel learning algorithm with a proposed l12-norm regularization is applied to combine these CGKs optimally together and simultaneously train a set of action classifiers. We conduct a series of experiments on several public action datasets. Our approach achieves a comparable performance to the state-of-the-art approaches, which demonstrates the effectiveness of the two-graph model and the CGKs in recognizing human actions.

关键词： Human action recognition context-dependent graph kernels generalized multiple kernel learning two-graph model

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