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Learning to Cluster Faces on an Affinity Graph 32

Learning to Cluster Faces on an Affinity Graph

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

作者： Yang, Lei Zhan, Xiaohang Chen, Dapeng Yan, Junjie Loy, Chen Change Lin, Dahua Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Grp Ltd Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781728132938

Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which would involve prohibitive annotation cost. Hence, exploiting unlabeled data becomes an appealing alternative. Recent works have shown that clustering unlabeled faces is a promising approach, often leading to notable performance gains. Yet, how to effectively cluster, especially on a large-scale (i.e. million-level or above) dataset, remains an open question. A key challenge lies in the complex variations of cluster patterns, which make it difficult for conventional clustering methods to meet the needed accuracy. This work explores a novel approach, namely, learning to cluster instead of relying on hand-crafted criteria. Specifically, we propose a framework based on graph convolutional network, which combines a detection and a segmentation module to pinpoint face clusters. Experiments show that our method yields significantly more accurate face clusters, which, as a result, also lead to further performance gain in face recognition.

关键词： And Body Pose Categorization Deep Learning Face Gesture Grouping and Shape recognition: detection Retrieval Segmentation

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ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network 32

ESPNetv2: A Light-weight, Power Efficient, and General Purpo...

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

作者： Mehta, Sachin Rastegari, Mohammad Shapiro, Linda Hajishirzi, Hannaneh Univ Washington Seattle WA 98195 USA Allen Inst AI AI2 Seattle WA USA XNOR AI Seattle WA USA

ISBN: (纸本)9781728132938

We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on four different tasks: (I) object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network outperforms ESPNet by 4-5% and has 2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at https://***/sachmehta/ESPNetv2.

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

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Informative Object Annotations: Tell Me Something I Don't Know 32

Informative Object Annotations: Tell Me Something I Don't Kn...

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

作者： Bracha, Lior Chechik, Gal Bar Ilan Univ Ramat Gan Israel Bar Ilan Univ NVIDIA Res Ramat Gan Israel

ISBN: (纸本)9781728132938

Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and use this uncertainty reduction to rank candidate labels for describing an image. While the full estimation problem is intractable, we describe an efficient algorithm to approximate entropy reduction using a tree-structured graphical model. We evaluate our approach on the open-images dataset using a new evaluation set of 10K ground-truth ratings and find that it achieves similar to 65% agreement with human raters, close to the upper bound of inter-rater agreement and largely outperforming other unsupervised baselines.

关键词： Categorization recognition: detection Retrieval Scene Analysis and Understanding Statistical Lea Vision + Language

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Snapshot Distillation: Teacher-Student Optimization in One Generation 32

Snapshot Distillation: Teacher-Student Optimization in One G...

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

作者： Yang, Chenglin Xie, Lingxi Su, Chi Yuille, Alan L. Johns Hopkins Univ Baltimore MD 21218 USA Huawei Inc Noahs Ark Lab Shenzhen Guangdong Peoples R China Kingsoft Cloud Beijing Peoples R China

ISBN: (纸本)9781728132938

Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. Teacher-student optimization aims at providing complementary cues from a model trained previously, but these approaches are often considerably slow due to the pipeline of training a few generations in sequence, i.e., time complexity is increased by several times. This paper presents snapshot distillation (SD), the first framework which enables teacher-student optimization in one generation. The idea of SD is very simple: instead of borrowing supervision signals from previous generations, we extract such information from earlier epochs in the same generation, meanwhile make sure that the difference between teacher and student is sufficiently large so as to prevent under-fitting. To achieve this goal, we implement SD in a cyclic learning rate policy, in which the last snapshot of each cycle is used as the teacher for all iterations in the next cycle, and the teacher signal is smoothed to provide richer information. In standard image classification benchmarks such as CIFAR100 and ILSVRC2012, SD achieves consistent accuracy gain without heavy computational overheads. We also verify that models pre-trained with SD transfers well to object detection and semantic segmentation in the PascalVOC dataset.

关键词： Categorization Deep Learning recognition: detection Retrieval

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ArcFace: Additive Angular Margin Loss for Deep Face recognition 32

ArcFace: Additive Angular Margin Loss for Deep Face Recognit...

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

作者： Deng, Jiankang Guo, Jia Xue, Niannan Zafeiriou, Stefanos Imperial Coll London London England InsightFace London England FaceSoft London England

ISBN: (纸本)9781728132938

One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that can enhance the discriminative power. Centre loss penalises the distance between deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in the angular space and therefore penalises the angles between deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximise face class separability. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to its exact correspondence to geodesic distance on a hypersphere. We present arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks which includes a new large-scale image database with trillions of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead. To facilitate future research, code has been made available at: https://***/deepinsight/insightface

关键词： And Body Pose Biometrics Categorization Face Gesture recognition: detection Retrieval

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Modularized Textual Grounding for Counterfactual Resilience 32

Modularized Textual Grounding for Counterfactual Resilience

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

作者： Fang, Zhiyuan Kong, Shu Fowlkes, Charless Yang, Yezhou Arizona State Univ Tempe AZ 85287 USA Univ Calif Irvine Irvine CA 92717 USA

ISBN: (纸本)9781728132938

Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries. To achieve high grounding precision, current textual grounding methods heavily rely on large-scale training data with manual annotations at the pixel level. Such annotations are expensive to obtain and thus severely narrow the model's scope of real-world applications. Moreover, most of these methods sacrifice interpretability, generalizability, and they neglect the importance of being resilient to counterfactual inputs. To address these issues, we propose a visual grounding system which is 1) end-to-end trainable in a weakly supervised fashion with only image-level annotations, and 2) counterfactually resilient owing to the modular design. Specifically, we decompose textual descriptions into three levels: entity, semantic attribute, color information, and perform compositional grounding progressively. We validate our model through a series of experiments and demonstrate its improvement over the state-of-the-art methods. In particular, our model's performance not only surpasses other weakly/un-supervised methods and even approaches the strongly supervised ones, but also is interpretable for decision making and performs much better in face of counterfactual classes than all the others.

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

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Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification 32

Attribute-Driven Feature Disentangling and Temporal Aggregat...

引用

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

作者： Zhao, Yiru Shen, Xu Jin, Zhongming Lu, Hongtao Hua, Xian-sheng Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai Peoples R China Alibaba Grp Alibaba Damo Acad Hangzhou Peoples R China

ISBN: (纸本)9781728132938

Video-based person re-identification plays an important role in surveillance video analysis, expanding image-based methods by learning features of multiple frames. Most existing methods fuse features by temporal average-pooling, without exploring the different frame weights caused by various viewpoints, poses, and occlusions. In this paper, we propose an attribute-driven method for feature disentangling and frame re-weighting. The features of single frames are disentangled into groups of sub-features, each corresponds to specific semantic attributes. The sub-features are re-weighted by the confidence of attribute recognition and then aggregated at the temporal dimension as the final representation. By means of this strategy, the most informative regions of each frame are enhanced and contributes to a more discriminative sequence representation. Extensive ablation studies demonstrate the effectiveness of feature disentangling as well as temporal re-weighting. The experimental results on the iLIDS-VID, PRID-2011 and MARS datasets demonstrate that our proposed method outperforms existing state-of-the-art approaches.

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

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MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors 32

MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Obje...

引用

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

作者： Cai, Lile Zhao, Bin Wang, Zhe Lin, Jie Foo, Chuan Sheng Aly, Mohamed Sabry Chandrasekhar, Vijay ASTAR Inst Infocomm Res Singapore Singapore ASTAR Inst Microelect Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781728132938

Modern convolutional object detectors have improved the detection accuracy significantly, which in turn inspired the development of dedicated hardware accelerators to achieve real-time performance by exploiting inherent parallelism in the algorithm. Non-maximum suppression (NMS) is an indispensable operation in object detection. In stark contrast to most operations, the commonly-adopted GreedyNMS algorithm does not foster parallelism, which can be a major performance bottleneck. In this paper, we introduce MaxpoolNMS, a parallelizable alternative to the NMS algorithm, which is based on max-pooling classification score maps. By employing a novel multi-scale multi-channel max-pooling strategy, our method is 20 x faster than GreedyNMS while simultaneously achieves comparable accuracy, when quantified across various benchmarking datasets, i.e., MS COCO, KI17I and PASCAL VOC. Furthermore, our method is better suited for hardware-based acceleration than GreedyNMS.

关键词： Categorization recognition: detection Retrieval Robotics + Driving Vision Applications and Systems

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The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos 32

The Pros and Cons: Rank-aware Temporal Attention for Skill D...

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

作者： Doughty, Hazel Mayol-Cuevas, Walterio Damen, Dima Univ Bristol Bristol Avon England

ISBN: (纸本)9781728132938

We present a new model to determine relative skill from long videos, through learnable temporal attention modules. Skill determination is formulated as a ranking problem, making it suitable for common and generic tasks. However for long videos, parts of the video are irrelevant for assessing skill, and there may be variability in the skill exhibited throughout a video. We therefore propose a method which assesses the relative overall level of skill in a long video by attending to its skill-relevant parts. Our approach trains temporal attention modules, learned with only video-level supervision, using a novel rank-aware loss function. In addition to attending to task-relevant video parts, our proposed loss jointly trains two attention modules to separately attend to video parts which are indicative of higher (pros)and lower (cons) skill. We evaluate our approach on the EPIC-Skills dataset and additionally annotate a larger dataset from YouTube videos for skill determination with five previously unexplored tasks. Our method outperforms previous approaches and classic softmax attention on both datasets by over 4% pairwise accuracy, and as much as 12% on individual tasks. We also demonstrate our model's ability to attend to rank-aware parts of the video.

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

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Seamless Scene Segmentation 32

Seamless Scene Segmentation

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

作者： Porzi, Lorenzo Bulo, Samuel Rota Colovic, Aleksander Kontschieder, Peter Mapillary Res Malmo Sweden

ISBN: (纸本)9781728132938

In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a lightweight DeepLab-like module. As additional contribution we review the panoptic metric and propose an alternative that overcomes its limitations when evaluating non-instance categories. Our proposed network architecture yields state-of-the-art results on three challenging street-level datasets,i.e. Cityscapes,Indian Driving Dataset and Mapillary Vistas.

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

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