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检索条件"主题词=Recognition: Detection"

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Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training 32

Pyramidal Person Re-IDentification via Multi-Loss Dynamic Tr...

引用

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

作者： Zheng, Feng Deng, Cheng Sun, Xing Jiang, Xinyang Guo, Xiaowei Yu, Zongqiao Huang, Feiyue Ji, Rongrong Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China YouTu Lab Tencent Shanghai Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Peoples R China Peng Cheng Lab Shenzhen Peoples R China YouTu Lab Shanghai Peoples R China

ISBN: (纸本)9781728132938

Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other. However, due to the challenging practical scenarios, current detection models often produce inaccurate bounding boxes, which inevitably degenerate the performance of existing Re-ID algorithms. In this paper, we propose a novel coarse-to-fine pyramid model to relax the need of bounding boxes, which not only incorporates local and global information, but also integrates the gradual cues between them. The pyramid model is able to match at different scales and then search for the correct image of the same identity, even when the image pairs are not aligned. In addition, in order to learn discriminative identity representation, we explore a dynamic training scheme to seamlessly unify two losses and extract appropriate shared information between them. Experimental results clearly demonstrate that the proposed method achieves the state-of-the-art results on three datasets. Especially, our approach exceeds the current best method by 9.5% on the most challenging CUHK03 dataset.

关键词： Categorization Deep Learning Motion and Tracking recognition: detection Representati Retrieval Vision Applications and Systems

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Explore-Exploit Graph Traversal for Image Retrieval 32

Explore-Exploit Graph Traversal for Image Retrieval

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

作者： Chang, Cheng Yu, Guangwei Liu, Chundi Volkovs, Maksims Layer6 AI Toronto ON Canada

ISBN: (纸本)9781728132938

We propose a novel graph-based approach for image retrieval. Given a nearest neighbor graph produced by the global descriptor model, we traverse it by alternating between exploit and explore steps. The exploit step maximally utilizes the immediate neighborhood of each vertex, while the explore step traverses vertices that are farther away in the descriptor space. By combining these two steps we can better capture the underlying image manifold, and successfully retrieve relevant images that are visually dissimilar to the query. Our traversal algorithm is conceptually simple, has few tunable parameters and can be implemented with basic data structures. This enables fast real-time inference for previously unseen queries with minimal memory overhead. Despite relative simplicity, we show highly competitive results on multiple public benchmarks, including the largest image retrieval dataset that is currently publicly available.

关键词： Categorization recognition: detection Retrieval

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Gradient Matching Generative Networks for Zero-Shot Learning 32

Gradient Matching Generative Networks for Zero-Shot Learning

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

作者： Sariyildiz, Mert Bulent Cinbis, Ramazan Gokberk Bilkent Univ Dept Comp Engn Ankara Turkey Middle East Tech Univ METU Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781728132938

Zero-shot learning (ZSL) is one of the most promising problems where substantial progress can potentially be achieved through unsupervised learning, due to distributional differences between supervised and zero-shot classes. For this reason, several works investigate the incorporation of discriminative domain adaptation techniques into ZSL, which, however, lead to modest improvements in ZSL accuracy. In contrast, we propose a generative model that can naturally learn from unsupervised examples, and synthesize training examples for unseen classes purely based on their class embeddings, and therefore, reduce the zero-shot learning problem into a supervised classification task. The proposed approach consists of two important components: (i) a conditional Generative Adversarial Network that learns to produce samples that mimic the characteristics of unsupervised data examples, and (ii) the Gradient Matching (GM) loss that measures the quality of the gradient signal obtained from the synthesized examples. Using our GM loss formulation, we enforce the generator to produce examples from which accurate classifiers can be trained. Experimental results on several ZSL benchmark datasets show that our approach leads to significant improvements over the state of the art in generalized zero-shot classification.

关键词： Categorization recognition: detection Retrieval Vision + Language

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Amodal Instance Segmentation with KINS Dataset 32

Amodal Instance Segmentation with KINS Dataset

引用

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

作者： Qi, Lu Jiang, Li Liu, Shu Shen, Xiaoyong Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Tencent YouTu Lab Shenzhen Peoples R China

ISBN: (纸本)9781728132938

Amodal instance segmentation, a new direction of instance segmentation, aims to segment each object instance involving its invisible, occluded parts to imitate human ability. This task requires to reason objects' complex structure. Despite important and futuristic, this task lacks data with large-scale and detailed annotation, due to the difficulty of correctly and consistently labeling invisible parts, which creates the huge barrier to explore the frontier of visual recognition. In this paper, we augment KITTI with more instance pixel-level annotation for 8 categories, which we call KITTI INStance dataset (KINS). We propose the network structure to reason invisible parts via a new multi-task framework with Multi-Level Coding (MLC), which combines information in various recognition levels. Extensive experiments show that our MLC effectively improves both amodal and inmodal segmentation. The KINS dataset and our proposed method are made publicly available.

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

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FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery 32

FineGAN: Unsupervised Hierarchical Disentanglement for Fine-...

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

作者： Singh, Krishna Kumar Ojha, Utkarsh Lee, Yong Jae Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781728132938

We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories. To disentangle the factors without supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy. Through extensive experiments, we show that FineGAN achieves the desired disentanglement to generate realistic and diverse images belonging to fine-grained classes of birds, dogs, and cars. Using FineGAN's automatically learned features, we also cluster real images as a first attempt at solving the novel problem of unsupen iced fine-grained object category discovery. Our code/models/demo can be found at https://***/kkanshul/fingan

关键词： Categorization Image and Video Synthesis recognition: detection Retrieval

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PIEs: Pose Invariant Embeddings 32

PIEs: Pose Invariant Embeddings

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

作者： Ho, Chih-Hui Morgado, Pedro Persekian, Amir Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781728132938

The role of pose invariance in image recognition and retrieval is studied. A taxonomic classification of embeddings, according to their level of invariance, is introduced and used to clarify connections between existing embeddings, identify missing approaches, and propose invariant generalizations. This leads to a new family of pose invariant embeddings (PIEs), derived from existing approaches by a combination of two models, which follow from the interpretation of CNNs as estimators of class posterior probabilities: a view-to-object model and an object-to-class model. The new pose-invariant models are shown to have interesting properties, both theoretically and through experiments, where they outperform existing multiview approaches. Most notably, they achieve good performance for both 1) classification and retrieval, and 2) single and multiview inference. These are important properties for the design of real vision systems, where universal embeddings are preferable to task specific ones, and multiple images are usually not available at inference time. Finally, a new multiview dataset of real objects, imaged in the wild against complex backgrounds, is introduced. We believe that this is a much needed complement to the synthetic datasets in wide use and will contribute to the advancement of multiview recognition and retrieval.

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

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Effective Aesthetics Prediction with Multi-level Spatially Pooled Features 32

Effective Aesthetics Prediction with Multi-level Spatially P...

引用

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

作者： Hosu, Vlad Goldluecke, Bastian Saupe, Dietmar Univ Konstanz Constance Germany

ISBN: (纸本)9781728132938

We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database. While all previous approaches miss some of the information in the original images, due to taking small crops, down-scaling or warping the originals during training, we propose the first method that efficiently supports full resolution images as an input, and can be trained on variable input sizes. This allows us to significantly improve upon the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) of ground-truth mean opinion scores (MOS) from the existing best reported of 0.612 to 0.756. To achieve this performance, we extract multi-level spatially pooled (MLSP) features from all convolutional blocks of a pre-trained InceptionResNet-v2 network, and train a custom shallow Convolutional Neural Network (CNN) architecture on these new features.

关键词： Categorization Deep Learning Others recognition: detection Retrieval

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Few-Shot Learning with Localization in Realistic Settings 32

Few-Shot Learning with Localization in Realistic Settings

引用

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

作者： Wertheimer, Davis Hariharan, Bharath Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781728132938

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast to both extremes, real world recognition problems exhibit heavy-tailed class distributions, with cluttered scenes and a mix of coarse and fine-grained class distinctions. We show that prior methods designed for few-shot learning do not work out of the box in these challenging conditions, based on a new meta-iNat benchmark. We introduce three parameter-free improvements: (a) better training procedures based on adapting cross-validation to meta-learning, (b) novel architectures that localize objects using limited bounding box annotations before classification, and (c) simple parameter-free expansions of the feature space based on bilinear pooling. Together, these improvements double the accuracy of state-of-the-art models on meta-iNat while generalizing to prior benchmarks, complex neural architectures, and settings with substantial domain shift.

关键词： Categorization Deep Learning Grouping and Shape recognition: detection Retrieval Segmentation

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A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning 32

A Theoretically Sound Upper Bound on the Triplet Loss for Im...

引用

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

作者： Do, Thanh-Toan Tran, Toan Reid, Ian Kumar, Vijay Hoang, Tuan Carneiro, Gustavo Univ Liverpool Liverpool Merseyside England Univ Adelaide Adelaide SA Australia PARC Palo Alto CA USA Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9781728132938

We propose a method that substantially improves the efficiency of deep distance metric learning based on the optimization of the triplet loss function. One epoch of such training process based on a naive optimization of the triplet loss function has a run-time complexity O(N-3), where N is the number of training samples. Such optimization scales poorly, and the most common approach proposed to address this high complexity issue is based on sub-sampling the set of triplets needed for the training process. Another approach explored in the field relies on an ad-hoc linearization (in terms of N) of the triplet loss that introduces class centroids, which must be optimized using the whole training set for each mini-batch - this means that a naive implementation of this approach has run-time complexity O(N-2). This complexity issue is usually mitigated with poor, but computationally cheap, approximate centroid optimization methods. In this paper, we first propose a solid theory on the linearization of the triplet loss with the use of class centroids, where the main conclusion is that our new linear loss represents a tight upper-bound to the triplet loss. Furthermore, based on the theory above, we propose a training algorithm that no longer requires the centroid optimization step, which means that our approach is the first in the field with a guaranteed linear run-time complexity. We show that the training of deep distance metric learning methods using the proposed upper-bound is substantially faster than triplet-based methods, while producing competitive retrieval accuracy results on benchmark datasets (CUB-200-2011 and CAR196).

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

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Deep Dual Relation Modeling for Egocentric Interaction recognition 32

Deep Dual Relation Modeling for Egocentric Interaction Recog...

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

作者： Li, Haoxin Cai, Yijun Zheng, Wei-Shi Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Peng Cheng Lab Shenzhen 518005 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9781728132938

Egocentricinteractionrecognitionaims to recognize the camera wearer's interactionswith the interactorwho faces the camera wearer in egocentric videos. In such a humanhuman interactionanalysisproblem, it is crucial to explore the relationsbetween the camera wearerand the interactor. However most existing works directly model the interactions as a whole and lack modeling the relations between the two interactingpersons. To exploit the strong relations for egocentric interactionrecognition,we introducea dual relation modelingframework which learns to model the relations between the camera wearerand the interactorbased on the individual action representationsof the two persons. Specifically, we develop a novel interactive LSTM module, the key component of our framework, to explicitly model the relations between the two interactingpersons based on their individual action representations,which are collaboratively learned with an interactorattention module and a global-localmotion module. Experimental results on three egocentric interactiondatasetsshow the effectiveness ofour method and advantage over state-of-the-arts.

关键词： Action recognition Categorization recognition: detection Retrieval Video Analytics

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