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UPSNet: A Unified Panoptic Segmentation Network 32

UPSNet: A Unified Panoptic Segmentation Network

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

作者： Xiong, Yuwen Liao, Renjie Zhao, Hengshuang Hu, Rui Bai, Min Yumer, Ersin Urtasun, Raquel Uber ATG Toronto ON Canada Univ Toronto Toronto ON Canada Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network, we first design a deformable convolution based semantic segmentation head and a Mask R-CNN style instance segmentation head which solve these two subtasks simultaneously. More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification. It first leverages the logits from the previous two heads and then innovatively expands the representation for enabling prediction of an extra unknown class which helps better resolve the conflicts between semantic and instance segmentation. Additionally, it handles the challenge caused by the varying number of instances and permits back propagation to the bottom modules in an end-to-end manner. Extensive experimental results on Cityscapes, COCO and our internal dataset demonstrate that our UPSNet achieves state-of-the-art performance with much faster inference. Code has been made available at: https://***/uber-research/UPSNet.

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

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Improving Transferability of Adversarial Examples with Input Diversity 32

Improving Transferability of Adversarial Examples with Input...

引用

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

作者： Xie, Cihang Zhang, Zhishuai Zhou, Yuyin Bai, Song Wang, Jianyu Ren, Zhou Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Univ Oxford Oxford England Baidu Res Beijing Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9781728132938

Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples - crafted by adding human-imperceptible perturbations to clean images. However, most of the existing adversarial attacks only achieve relatively low success rates under the challenging black-box setting, where the attackers have no knowledge of the model structure and parameters. To this end, we propose to improve the transferability of adversarial examples by creating diverse input patterns. Instead of only using the original images to generate adversarial examples, our method applies random transformations to the input images at each iteration. Extensive experiments on ImageNet show that the proposed attack method can generate adversarial examples that transfer much better to different networks than existing baselines. By evaluating our method against top defense solutions and official baselines from NIPS 2017 adversarial competition, the enhanced attack reaches an average success rate of 73.0%, which outperforms the top-1 attack submission in the NIPS competition by a large margin of 6.6%. We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future.

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

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PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval 32

PCAN: 3D Attention Map Learning Using Contextual Information...

引用

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

作者： Zhang, Wenxiao Xiao, Chunxia Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728132938

Point cloud based retrieval for place recognition is an emerging problem in vision field. The main challenge is how to find an efficient way to encode the local features into a discriminative global descriptor. In this paper, we propose a Point Contextual Attention Network (PCAN), which can predict the significance of each local point feature based on point context. Our network makes it possible to pay more attention to the task-relevent features when aggregating local features. Experiments on various benchmark datasets show that the proposed network can provide outperformance than current state-of-the-art approaches.

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

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

作者： Wang, Hao Gong, Dihong Li, Zhifeng Liu, Wei Tencent AI Lab Bellevue WA 98004 USA

ISBN: (纸本)9781728132938

There has been an increasing research interest in age-invariant face recognition. However, matching faces with big age gaps remains a challenging problem, primarily due to the significant discrepancy of face appearance caused by aging. To reduce such discrepancy, in this paper we present a novel algorithm to remove age-related components from features mixed with both identity and age information. Specifically, we factorize a mixed face feature into two uncorrelated components: identity-dependent component and age-dependent component, where the identity-dependent component contains information that is useful for face recognition. To implement this idea, we propose the Decorrelated Adversarial Learning (DAL) algorithm, where a Canonical Mapping Module (CMM) is introduced to find maximum correlation of the paired features generated by the backbone network, while the backbone network and the factorization module are trained to generate features reducing the correlation. Thus, the proposed model learns the decomposed features of age and identity whose correlation is significantly reduced. Simultaneously, the identity-dependent feature and the age-dependent feature are supervised by ID and age preserving signals respectively to ensure they contain the correct information. Extensive experiments have been conducted on the popular public-domain face aging datasets (FG-NET, MORPH Album 2, and CACD-VS) to demonstrate the effectiveness of the proposed approach.

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

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Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image recognition 32

Looking for the Devil in the Details: Learning Trilinear Att...

引用

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

作者： Zheng, Heliang Fu, Jianlong Zha, Zheng-Jun Luo, Jiebo Univ Sci & Technol China Hefei Peoples R China Microsoft Res Beijing Peoples R China Univ Rochester Rochester NY USA

ISBN: (纸本)9781728132938

Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localize and amplify significant parts to learn fine-grained details, which often suffer from a limited number of parts and heavy computational cost. In this paper, we propose to learn such fine-grained features from hundreds of part proposals by Trilinear Attention Sampling Network (TASN) in an efficient teacher-student manner. Specifically, TASN consists of 1) a trilinear attention module, which generates attention maps by modeling the inter-channel relationships, 2) an attention-based sampler which highlights attended parts with high resolution, and 3) a feature distiller, which distills part features into an object-level feature by weight sharing and feature preserving strategies. Extensive experiments verify that TASN yields the best performance under the same settings with the most competitive approaches, in iNaturalist-2017, CUB-Bird, and Stanford-Cars datasets.

关键词： Categorization recognition: detection Representation Learning Retrieval

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Point in, Box out: Beyond Counting Persons in Crowds 32

Point in, Box out: Beyond Counting Persons in Crowds

引用

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

作者： Liu, Yuting Shi, Miaojing Zhao, Qijun Wang, Xiaofang Sichuan Univ Coll Comp Sci Chengdu Peoples R China Univ Rennes CNRS INRIA IRISA Rennes France

ISBN: (纸本)9781728132938

Modern crowd counting methods usually employ deep neural networks (DNN) to estimate crowd counts via density regression. Despite their significant improvements, the regression-based methods are incapable of providing the detection of individuals in crowds. The detection-based methods, on the other hand, have not been largely explored in recent trends of crowd counting due to the needs for expensive bounding box annotations. In this work, we instead propose a new deep detection network with only point supervision required. It can simultaneously detect the size and location of human heads and count them in crowds. We first mine useful person size information from point-level annotations and initialize the pseudo ground truth bounding boxes. An online updating scheme is introduced to refine the pseudo ground truth during training;while a locally-constrained regression loss is designed to provide additional constraints on the size of the predicted boxes in a local neighborhood. In the end, we propose a curriculum learning strategy to train the network from images of relatively accurate and easy pseudo ground truth first. Extensive experiments are conducted in both detection and counting tasks on several standard benchmarks, e.g. ShanghaiTech, UCF_CC_50, WiderFace, and TRANCOS datasets, and the results show the superiority of our method over the state-of-the-art.

关键词： Categorization recognition: detection Retrieval

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Quantization Networks 32

Quantization Networks

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

作者： Yang, Jiwei Shen, Xu Xing, Jun Tian, Xinmei Li, Houqiang Deng, Bing Huang, Jianqiang Hua, Xian-sheng Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Peoples R China Alibaba Grp Damo Acad Hangzhou Peoples R China Univ Southern Calif Inst Creat Technol Los Angeles CA 90007 USA

ISBN: (纸本)9781728132938

Although deep neural networks are highly effective, their high computational and memory costs severely hinder their applications to portable devices. As a consequence, low-bit quantization,which converts a full-precision neural network into a low-bitwidth integer version, has been an active and promising research topic. Existing methods formulate the low-bit quantization of networks as an approximation or optimization problem. Approximation-based methods con-front the gradient mismatch problem, while optimization based methods are only suitable for quantizing weights and can introduce high computational cost during the training stage. In this paper we provide a simple and uniform way for weights and activations quantization by formulating it as a differentiable non-linear function. The quantization function is represented as a linear combination of several Sigmoid functions with learnable biases and scales that could be learned in a lossless and end-to-end manner via continuous relaxation of the steepness of Sigmoid functions. Extensive experiments on image classification and object detection tasks show that our quantization networks outper-form state-of-the-art methods. We believe that the proposed method will shed new lights on the interpretation of neural network quantization.

关键词： Categorization Deep Learning recognition: detection Retrieval

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From recognition to Cognition: Visual Commonsense Reasoning 32

From Recognition to Cognition: Visual Commonsense Reasoning

引用

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

作者： Zellers, Rowan Bisk, Yonatan Farhadi, Ali Choi, Yejin Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA Allen Inst Artificial Intelligence Seattle WA USA

ISBN: (纸本)9781728132938

Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people's actions, goals, and mental states. While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world. We formalize this task as Visual Commonsense Reasoning. Given a challenging question about an image, a machine must answer correctly and then provide a rationale justifying its answer. Next, we introduce a new dataset, VCR, consisting of 290k multiple choice QA problems derived from 110k movie scenes. The key recipe for generating non-trivial and high-quality problems at scale is Adversarial Matching, a new approach to transform rich annotations into multiple choice questions with minimal bias. Experimental results show that while humans find VCR easy (over 90% accuracy), state-of-the-art vision models struggle (similar to 45%). To move towards cognition-level understanding, we present a new reasoning engine, recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. R2C helps narrow the gap between humans and machines (similar to 65%);still, the challenge is far from solved, and we provide analysis that suggests avenues for future work.

关键词： Categorization recognition: detection Retrieval Scene Analysis and Understanding Vision + Language Visual Reasonin

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Interaction-and-Aggregation Network for Person Re-identification 32

Interaction-and-Aggregation Network for Person Re-identifica...

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

作者： Hou, Ruibing Ma, Bingpeng Chang, Hong Gu, Xinqian Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

ISBN: (纸本)9781728132938

Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However CNNs are inherently limited in modeling the law variations in person pose and scale due to their fixed geometric structures. In this paper, we propose a novel network structure, interaction-and-Aggregation (IA), to enhance the feature representation capability of CNNs. Firstly Spatial IA (SIA) module is introduced. It models the interdependencies between spatial features and then aggregates the correlated features corresponding to the same body parts. Unlike CNNs which extract features from fixed rectangle regions, SIA can adaptively determine the receptive fields according to the input person pose and scale. Secondly, we introduce Channel IA (CIA) module which selectively aggregates channel features to enhance the feature representation, especially for smallscale visual cues. Further IA network can he constructed by inserting IA blocks into CNNs at any depth. We validate the effectiveness of our model for person reID by demonstrating its superiority over state-of-the-art methods on three benchmark datasets.

关键词： Categorization recognition: detection Retrieval

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Compressing Unknown Images with Product Quantizer for Efficient Zero-Shot Classification 32

Compressing Unknown Images with Product Quantizer for Effici...

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

作者： Li, Jin Lan, Xuguang Liu, Yang Wang, Le Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Xi An Jiao Tong Univ Natl Engn Lab Visual Informat Proc & Applicat Xian Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781728132938

For Zero-Shot Learning (ZSL), the Nearest Neighbor (NN) search is generally conducted for classification, which may cause unacceptable computational complexity for large-scale datasets. To compress zero-shot classes by the trained quantizer for efficient search, it tends to induce large quantization error because distributions between seen and unseen classes are different. However, as semantic attributes of classes are available in ZSL, both seen and unseen classes have the same distribution for one specific property, e.g., animals have or not have spots. Based on this intuition, a Product Quantization Zero-Shot Learning (PQZSL) method is proposed to learn embeddings as well as quantizers to compress visual features into compact codes for Approximate NN (ANN) search. Particularly, visual features are projected into an orthogonal semantic space, and then the Product Quantization (PQ) is utilized to quantize individual properties. Experimental results on five benchmark datasets demonstrate that unseen classes are represented by the Cartesian product of quantized properties with little quantization error. As classes in orthogonal common space are more discriminative, the classification based on PQZSL achieves state-of-the-art performance in Generalized Zero-Shot Learning (GZSL) task, meanwhile, the speed of ANN search is 10-100 times higher than traditional NN search.

关键词： Big Data Categorization Large Scale Methods recognition: detection Retrieval

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