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检索条件"主题词=Recognition: Detection"

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Triangulation Learning Network: from Monocular to Stereo 3D Object detection 32

Triangulation Learning Network: from Monocular to Stereo 3D ...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Qin, Zengyi Wang, Jinglu Lu, Yan Tsinghua Univ Beijing Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781728132938

In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information. Different from previous methods using pixel-level depth maps, we propose employing 3D anchors to explicitly construct object-level correspondences between the regions of interest in stereo images, from which the deep neural network learns to detect and triangulate the targeted object in 3D space. We also introduce a cost-efficient channel reweighting strategy that enhances representational features and weakens noisy signals to facilitate the learning process. All of these are flexibly integrated into a solid baseline detector that uses monocular images. We demonstrate that both the monocular baseline and the stereo triangulation learning network outperform the prior state-of-the-arts in 3D object detection and localization on the challenging KITTI dataset.

关键词： 3D from Multiview and Sensors Categorization recognition: detection Retrieval

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On Exploring Undetermined Relationships for Visual Relationship detection 32

On Exploring Undetermined Relationships for Visual Relations...

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Zhan, Yibing Yu, Jun Yu, Ting Tao, Dacheng Hangzhou Dianzi Univ Sch Comp Sci & Technol Key Lab Complex Syst Modeling & Simulat Hangzhou Peoples R China Univ Sydney FEIT Sch Comp Sci UBTECH Sydney AI Ctr Darlington NSW 2008 Australia

ISBN: (纸本)9781728132938

In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undetermined relationships. Although a vast body of literature exists,few methods exploit these undetermined relationships for visual relationship detection. In this paper, we explore the beneficial effect of undetermined relationship son visual relationship detection. We propose a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieve great improvements in relationship detection. In detail, our MF-URLN automatically generates undetermined relationships by comparing object pairs with human-notated data according to a designed criterion. Then, the MF-URLN extracts and fuses features of object pairs from three complementary modals: visual, spatial,and linguistic modals. Further the MF-URLN proposes two correlated subnetworks: one subnetwork decides the determinate confidence, and the other predicts the relationships. We evaluate the MF-URLN on two datasets: the Visual Relationship detection (VRD) and the Visual Genome (VG) datasets. The experimental results compared with state-of-the-art methods verify the significant improvements made by the undetermined relationships, e.g., the top-50 relation detection recall improves from 19.5% to 23.9% on the VRD dataset.

关键词： Categorization recognition: detection Retrieval Scene Analysis and Understanding

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Learning Shape-Aware Embedding for Scene Text detection 32

Learning Shape-Aware Embedding for Scene Text Detection

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Tian, Zhuotao Shu, Michelle Lyu, Pengyuan Li, Ruiyu Zhou, Chao Shen, Xiaoyong Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA Tencent YouTu Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728132938

We address the problem of detecting scene text in arbitrary shapes, which is a challenging task due to the high variety and complexity of the scene. We treat text detection as instance segmentation and propose a segmentation-based framework, which extracts each text instance as an independent connected component. To distinguish among different text instances, our method maps pixels onto an embedding space where pixels belonging to the same text are encouraged to appear closer to each other and vise versa. In addition, we introduce a shape-aware Loss to make training adaptively accommodate various aspect ratios of text instances and even the tiny gaps among them. A new post-processing pipeline yields precise bounding box prediction. Experimental results on three challenging datasets (ICDAR15 [20], MSRA-TD500 [55]and CTW1500 [32] demonstrate the effectiveness of our work.

关键词： Categorization recognition: detection Retrieval Vision Applications and Systems

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TACNet: Transition-Aware Context Network for Spatio-Temporal Action detection 32

TACNet: Transition-Aware Context Network for Spatio-Temporal...

引用

IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Song, Lin Zhang, Shiwei Yu, Gang Sun, Hongbin Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Huazhong Univ Sci & Technol Artificial Intelligence & Automat Wuhan Peoples R China Megvii Inc Face Beijing Peoples R China

ISBN: (纸本)9781728132938

Current state-of-the-art approaches for spatio-temporal action detection have achieved impressive results but remain unsatisfactoryfor temporal extent detection. The main reason comes from that, there are some ambiguous states similar to the real actions which may be treated as target actions even by a well-trained network. In this paper;we define these ambiguous samples as "transitional states", and propose a Transition -Aware Context Network (TACNet) to distinguish transitional states. The proposed TACNet includes two main components, i.e., temporal context detectorand transition -aware classifier The temporal context detector can extract long-term context information with constant time complexity by constructing a recurrent network. The transition-aware classifier can further- distinguish transitional states by classifying action and transitional states simultaneously. Therefore, the proposed TACNet can substantially improve the performance of spatio-temporal action detection. We extensively evaluate the proposed TACNet on UCF101-24 and J-HMDB datasets. The experimental results demonstrate that TACNet obtains competitive performance on JHMDB and significantly outperforms the state-of-the-art methods on the untrimmed UCF101-24 in terms of both frame-mAP and video-mAP.

关键词： Categorization recognition: detection Retrieval Video Analytics

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Joint Manifold Diffusion for Combining Predictions on Decoupled Observations 32

Joint Manifold Diffusion for Combining Predictions on Decoup...

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Kim, Kwang In Chang, Hyung Jin UNIST Ulsan South Korea Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9781728132938

We present a new predictor combination algorithm that improves a given task predictor based on potentially relevant reference predictors. Existing approaches are limited in that, to discover the underlying task dependence, they either require known parametric forms of all predictors or access to a single fixed dataset on which all predictors are jointly evaluated. To overcome these limitations, we design a new non-parametric task dependence estimation procedure that automatically aligns evaluations of heterogeneous predictors across disjoint feature sets. Our algorithm is instantiated as a robust manifold diffusion process that jointly refines the estimated predictor alignments and the corresponding task dependence. We apply this algorithm to the relative attributes ranking problem and demonstrate that it not only broadens the application range of predictor combination approaches but also outperforms existing methods even when applied to classical predictor combination settings.

关键词： Categorization recognition: detection Representation Learning Retrieval Statistical Learning Vision Applications and

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引用

IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wang, Xun Han, Xintong Huang, Weiling Dong, Dengke Scott, Matthew R. Malong Technol Shenzhen Peoples R China Shenzhen Malong Artificial Intelligence Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9781728132938

A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning. In this paper we provide a general weighting framework for understanding recent pair-based loss functions. Our contributions are three-fold: (1) we establish a General Pair Weighting (GPW) framework,which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis,providing a powerful tool for understanding recent pair-based loss functions;(2) we show that with GPW various existing pair-based methods can be compared and discussed comprehensively, with clear differences and key limitations identified;(3) we propose a new loss called multi-similarity loss (MS loss) under the GPW, which is implemented in two iterative steps (i.e., mining and weighting). This allows it to fully consider three similarities for pair weighting, providing a more principled approach for collecting and weighting informative pairs. Finally,the proposed MS loss obtains new state-of-the-art performance on four image retrieval benchmarks, where it outperforms the most recent approaches, such as ABE[14] and HTL [4], by a large margin, e.g., 60.6% -> 65.7% on CUB200, and 80.9% -> 88.0% on In-Shop Clothes Retrieval dataset at Recall@1.

关键词： Categorization Deep Learning recognition: detection Representation Learning Retrieval

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Progressive Feature Alignment for Unsupervised Domain Adaptation 32

Progressive Feature Alignment for Unsupervised Domain Adapta...

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Chen, Chaoqi Xie, Weiping Huang, Wenbing Rong, Yu Ding, Xinghao Huang, Yue Xu, Tingyang Huang, Junzhou Xiamen Univ Fujian Key Lab Sensing & Comp Smart City Sch Informat Sci & Engn Xiamen Peoples R China Tencent AI Lab Bellevue WA 98004 USA

ISBN: (纸本)9781728132938

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fullyunlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets.

关键词： Categorization Deep Learning recognition: detection Representation Learning Retrieval

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Learning to Transfer Examples for Partial Domain Adaptation 32

Learning to Transfer Examples for Partial Domain Adaptation

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Cao, Zhangjie You, Kaichao Long, Mingsheng Wang, Jianmin Yang, Qiang Tsinghua Univ KLiss MOE Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ Res Ctr Big Data Beijing Peoples R China Beijing Key Lab Ind Big Data Syst & Applicat Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. In the era of Big Data, large-scale labeled datasets are readily available, stimulating the interest in partial domain adaptation (PDA), which transfers a recognizes from a large labeled domain to a small unlabeled domain. It extends standard domain adaptation to the scenario where target labels are only a subset of source labels. Under the condition that target labels are unknown, the key challenges of PDA are how to transfer relevant examples in the shared classes to promote positive transfer and how to ignore irrelevant ones in the source domain to mitigate negative transfer. In this work, we propose a unified approach to PDA, Example Transfer Network (ETN), which jointly learns domain-invariant representations across domains and a progressive weighting scheme to quantify the transferability of source examples. A thorough evaluation on several benchmark datasets shows that ETN consistently achieves state-of-the-art results for various partial domain adaptation tasks.

关键词： Categorization Deep Learning recognition: detection Retrieval

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Deep RNN Framework for Visual Sequential Applications 32

Deep RNN Framework for Visual Sequential Applications

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Pang, Bo Zha, Kaiwen Cao, Hanwen Shi, Chen Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence AI Inst Shanghai Peoples R China

ISBN: (纸本)9781728132938

Extracting temporal and representation features efficiently plays a pivotal role in understanding visual sequence information. To deal with this, we propose a new recurrent neural framework that can be stacked deep effectively. There are mainly two novel designs in our deep RNN framework: one is a new RNN module called Context Bridge Module (CBM) which splits the information flowing along the sequence (temporal direction) and along depth (spatial representation direction), making it easier to train when building deep by balancing these two directions;the other is the Overlap Coherence Training Scheme that reduces the training complexity for long visual sequential tasks on account of the limitation of computing resources. We provide empirical evidence to show that our deep RNN framework is easy to optimize and can gain accuracy from the increased depth on several visual sequence problems. On these tasks, we evaluate our deep RNN framework with 15 layers, 7x than conventional RNN networks, but it is still easy to train. Our deep framework achieves more than 11% relative improvements over shallow RNN models on Kinetics, UCF-101, and HMDB-51 for video classification. For auxiliary annotation, after replacing the shallow RNN part of Polygon-RNN with our 15-layer deep CBM, the performance improves by 14.7%. For video future prediction, our deep RNN improves the state-of-the-art shallow model's performance by 2.4% on PSNR and SSIM.

关键词： Categorization Deep Learning recognition: detection Retrieval Video Analytics

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Local Features and Visual Words Emerge in Activations 32

Local Features and Visual Words Emerge in Activations

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Simeoni, Oriane Avrithis, Yannis Chum, Ondrej Univ Rennes INRIA CNRS IRISA Rennes France Czech Tech Univ VRG FEE Prague Czech Republic

ISBN: (纸本)9781728132938

We propose a novel method of deep spatial matching (DSM) for image retrieval. Initial ranking is based on image descriptors extracted from convolutional neural network activations by global pooling, as in recent state-of-the-art work. However, the same sparse 3D activation tensor is also approximated by a collection of local features. These local features are then robustly matched to approximate the optimal alignment of the tensors. This happens without any network modification, additional layers or training. No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process. We experimentally show that the proposed method achieves the state-of-the-art performance on standard benchmarks across different network architectures and different global pooling methods. The highest gain in performance is achieved when diffusion on the nearest-neighbor graph of global descriptors is initiated from spatially verified images.

关键词： Categorization Computer Vision Theory recognition: detection Retrieval

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