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Network Space Search for Pareto-Efficient Spaces

Network Space Search for Pareto-Efficient Spaces

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

作者： Hong, Min-Fong Chen, Hao-Yun Chen, Min-Hung Xu, Yu-Syuan Kuo, Hsien-Kai Tsai, Yi-Min Chen, Hung-Jen Jou, Kevin MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9781665448994

Network spaces have been known as a critical factor in both handcrafted network designs or defining search spaces for Neural Architecture Search (NAS). However, an effective space involves tremendous prior knowledge and/or manual effort, and additional constraints are required to discover efficiency-aware architectures. In this paper, we define a new problem, Network Space Search (NSS), as searching for favorable network spaces instead of a single architecture. We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones. The resultant network spaces, named Elite Spaces, are discovered from Expanded Search Space with minimal human expertise imposed. The Pareto-efficient Elite Spaces are aligned with the Pareto front under various complexity constraints and can be further served as NAS search spaces, benefiting differentiable NAS approaches (e.g. In CIFAR-100, an averagely 2.3% lower error rate and 3.7% closer to target constraint than the baseline with around 90% fewer samples required to find satisfactory networks). Moreover, our NSS approach is capable of searching for superior spaces in future unexplored spaces, revealing great potential in searching for network spaces automatically.

关键词： computer vision Error analysis conferences Focusing Manuals computer architecture Search problems

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Rethinking Training Data for Mitigating Representation Biases in Action recognition

Rethinking Training Data for Mitigating Representation Biase...

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

作者： Hara, Kensho Ishikawa, Yuchi Kataoka, Hirokatsu Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

The purpose of this study is to train spatiotemporal 3D convolutional neural networks (3D CNNs) that properly leverage temporal information to recognize actions. Though 3D CNNs are an effective framework in action recognition, some studies showed the biases of video datasets for generic action recognition lead 3D CNNs to recognize not dynamic motions but static cues, such as objects, scenes, and people. On the other hand, video datasets for fine-grained action recognition, which classifies various actions in a specific domain, are expected to have small biases compared with the datasets for generic action recognition. In this study, we examine the biases of various video datasets, which include both generic and fine-grained action recognition tasks, for training 3D CNNs. Based on the results of experiments, the following conclusions could be obtained: (i) The representation biases learned from fine-grained action recognition datasets are smaller than those of generic action recognition datasets. (ii) The models pretrained on fine-grained action recognition datasets, of which the biases are small, leverage temporal information to recognize actions rather than static information. (iii) The models that leverage temporal information achieve better performance on fine-grained action recognition whereas the performance of the models pretrained on biased datasets is better on generic action recognition. We should evaluate models on both generic and fine-grained recognition datasets to properly evaluate their performance.

关键词： Training Solid modeling Three-dimensional displays Computational modeling Dynamics Training data Data models

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Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues

Joint Physical-Digital Facial Attack Detection Via Simulatin...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Xianhua He Dashuang Liang Song Yang Zhanlong Hao Hui Ma Binjie Mao Xi Li Yao Wang Pengfei Yan Ajian Liu Vision AI Department Meituan M.U.S.T MAIS CASIA China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. However, few methods are considered to integrate a model that simultaneously addresses both physical and digital attacks, implying the necessity to develop and maintain multiple models. To jointly detect physical and digital attacks within a single model, we propose an innovative approach that can adapt to any network architecture. Our approach mainly contains two types of data augmentation, which we call Simulated Physical Spoofing Clues augmentation (SPSC) and Simulated Digital Spoofing Clues augmentation (SDSC). SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types. Extensive experiments show that SPSC and SDSC can achieve state-of-the-art generalization in Protocols 2.1 and 2.2 of the UniAttackData dataset, respectively. Our method won first place in "Unified Physical-Digital Face Attack Detection" of the 5th Face Anti-spoofing Challenge@cvpr2024. Our final submission obtains 3.75% APCER, 0.93% BPCER, and 2.34% ACER, respectively. Our code is available at https://***/Xianhua-He/cvpr2024-face-anti-spoofing-challenge.

关键词： Adaptation models computer vision Protocols Codes Face recognition conferences Computational modeling

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Transformer-based Text Detection in the Wild

Transformer-based Text Detection in the Wild

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

作者： Raisi, Zobeir Naiel, Mohamed A. Younes, Georges Wardell, Steven Zelek, John S. Univ Waterloo Waterloo ON N2L 3G1 Canada ATS Automat Tooling Syst Inc Cambridge ON N3H 4R7 Canada

ISBN: (纸本)9781665448994

A major limitation to most state-of-the-art visual localization methods is their ineptitude to make use of ubiquitous signs and directions that are typically intuitive to humans. Localization methods can greatly benefit from a system capable of reasoning about a variety of cues beyond low-level features, such as street signs, store names, building directories, room numbers, etc. In this work, we tackle the problem of text detection in the wild, an essential step towards achieving text-based localization and mapping. While current state-of-the-art text detection methods employ ad-hoc solutions with complex multi-stage components to solve the problem, we propose a Transformer-based architecture inherently capable of dealing with multi-oriented texts in images. A central contribution to our work is the introduction of a loss function tailored to the rotated text detection problem that leverages a rotated version of a generalized intersection over union score to properly capture the rotated text regions. We evaluate our proposed model qualitatively and quantitatively on several challenging datasets namely, ICDAR15, ICDAR17, and MSRA-TD500, and show that it outperforms current state-of-the-art methods in text detection in the wild.

关键词： Location awareness Visualization computer vision conferences Buildings computer architecture Detectors

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Progressive Knowledge-Embedded Unified Perceptual Parsing for Scene Understanding

Progressive Knowledge-Embedded Unified Perceptual Parsing fo...

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

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665448994

Human can naturally understand scenes in depth with the help of various knowledge accumulated and by a comprehensive visual concept organization including category labels and different-level attributes. This inspires us to unify professional knowledge at different levels with deep neural network architectures progressively for scene understanding. Different from the general embedding approaches, we construct different knowledge graphs for different levels of vision tasks by organizing the rich visual concepts accordingly. We employ a gated graph neural network and relational graph convolutional networks to propagate node messages for different levels of tasks and generate progressively different levels of knowledge representation through the graph. Compared with existing methods, our framework has a main appealing property leading to a novel progressive knowledge-embedded representation learning framework that incorporates different level knowledge graphs into the learning of networks at corresponding level. Extensive experiments on the widely used Broden+ dataset demonstrate the superiority of the proposed framework over other existing state-of-the-art methods.

关键词： Deep learning Visualization computer vision conferences Organizations Knowledge representation Logic gates

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IntegralAction: Pose-driven Feature Integration for Robust Human Action recognition in Videos

IntegralAction: Pose-driven Feature Integration for Robust H...

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

作者： Moon, Gyeongsik Kwon, Heeseung Lee, Kyoung Mu Cho, Minsu SNU ECE & ASRI Seoul South Korea ASRI Seoul South Korea POSTECH CSE Pohang South Korea AIGS Seoul South Korea

ISBN: (纸本)9781665448994

Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input. While being effective in exploiting contextual information around humans, e.g., human appearance and scene category, they are easily fooled by out-of-context action videos where the contexts do not exactly match with target actions. In contrast, pose-based methods, which take a sequence of human skeletons only as input, suffer from inaccurate pose estimation or ambiguity of human pose per se. Integrating these two approaches has turned out to be non-trivial;training a model with both appearance and pose ends up with a strong bias towards appearance and does not generalize well to unseen videos. To address this problem, we propose to learn pose-driven feature integration that dynamically combines appearance and pose streams by observing pose features on the fly. The main idea is to let the pose stream decide how much and which appearance information is used in integration based on whether the given pose information is reliable or not. We show that the proposed IntegralAction achieves highly robust performance across in-context and out-of-context action video datasets. The codes are available in here.

关键词： Training computer vision Image recognition conferences Pose estimation Information filters Skeleton

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DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing

DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous ...

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

作者： Fu, Minghan Liu, Huan Yu, Yankun Chen, Jun Wang, Keyan McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781665448994

Hazy images are often subject to color distortion, blurring, and other visible quality degradation. Some existing CNN-based methods have great performance on removing homogeneous haze, but they are not robust in non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the complicated haze distribution, texture details are easy to be lost during the dehazing process. Secondly, since the training pairs are hard to be collected, training on limited data can easily lead to over-fitting problem. To tackle these two issues, we introduce a novel dehazing network using 2D discrete wavelet transform, namely DW-GAN. Specifically, we propose a two-branch network to deal with the aforementioned problems. By utilizing wavelet transform in DWT branch, our proposed method can retain more high frequency knowledge in feature maps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted in the knowledge adaptation branch. Owing to the robust feature representations of ImageNet pre-training, the generalization ability of our network is improved dramatically. Finally, a patch-based discriminator is used to reduce artifacts of the restored images. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively.

关键词： Training Wavelet domain Image color analysis Frequency-domain analysis Generative adversarial networks Discrete wavelet transforms pattern recognition

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Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery

Superpixels and Graph Convolutional Neural Networks for Effi...

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

作者： Dadsetan, Saba Pichler, David Wilson, David Hovakimyan, Naira Hobbs, Jennifer Univ Pittsburgh Pittsburgh PA 15260 USA Intelinair Inc San Francisco CA USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Advances in remote sensing technology have led to the capture of massive amounts of data. Increased image resolution, more frequent revisit times, and additional spectral channels have created an explosion in the amount of data that is available to provide analyses and intelligence across domains, including agriculture. However, the processing of this data comes with a cost in terms of computation time and money, both of which must be considered when the goal of an algorithm is to provide real-time intelligence to improve efficiencies. Specifically, we seek to identify nutrient deficient areas from remotely sensed data to alert farmers to regions that require attention;detection of nutrient deficient areas is a key task in precision agriculture as farmers must quickly respond to struggling areas to protect their harvests. Past methods have focused on pixel-level classification (i.e. semantic segmentation) of the field to achieve these tasks, often using deep learning models with tens-of-millions of parameters. In contrast, we propose a much lighter graph-based method to perform node-based classification. We first use Simple Linear Iterative Cluster (SLIC) to produce super-pixels across the field. Then, to perform segmentation across the non-Euclidean domain of superpixels, we leverage a Graph Convolutional Neural Network (GCN). This model has 4-orders-of-magnitude fewer parameters than a CNN model and trains in a matter of minutes.

关键词： Image segmentation Semantics Agriculture Real-time systems Computational efficiency pattern recognition Convolutional neural networks

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Grounded, Controllable and Debiased Image Completion with Lexical Semantics

Grounded, Controllable and Debiased Image Completion with Le...

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

作者： Zhang, Shengyu Jiang, Tan Huang, Qinghao Tan, Ziqi Kuang, Kun Zhao, Zhou Tang, Siliang Yu, Jin Yang, Hongxia Yang, Yi Wu, Fei Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this paper, we present an approach, namely Lexical Semantic Image Completion (LSIC)(1), that may have potential applications in art, design, and heritage conservation, among several others. Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful to a grounded knowledge. To permit both grounded and controllable completion process, we advocate generating results faithful to both visual and lexical semantic context, i.e., the description of leaving holes or blank regions in the image (e.g., hole description). One major challenge for LSIC comes from modeling and aligning the structure of visual-semantic context and translating across different modalities. We devise multi-grained reasoning blocks to address this challenge. Another challenge relates to the unimodal biases, which occurs when the model generates plausible results without using the textual description. We devise an unsupervised unpaired-creation learning path that explicitly performs counterfactual thinking, i.e., what the complete image would be if given an unpaired text description to the incomplete image. A cycle consistency loss is devised to guarantee counterfactual faithfulness. We conduct extensive quantitative and qualitative experiments that reveal the strengths of LSIC in being grounded, controllable, and debiased.

关键词： Measurement Visualization computer vision conferences Semantics Process control Cognition

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A Robust MTMC Tracking System for AI-City Challenge 2021

A Robust MTMC Tracking System for AI-City Challenge 2021

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

作者： Ye, Jin Yang, Xipeng Kang, Shuai He, Yue Zhang, Weiming Huang, Leping Jiang, Minyue Zhang, Wei Shi, Yifeng Xia, Meng Tan, Xiao Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China

ISBN: (纸本)9781665448994

Multi-Target Multi-Camera tracking (MTMC) is an essential task in the intelligent city and traffic analysis. It is a great challenging task due to several problems such as heavy occlusions and appearance variance caused by various camera perspectives and congested vehicles. In this paper, we propose a practical framework for dealing with the MTMC problem. The proposed framework contains three stage. Firstly, in the vehicles detection and Re-ID stage, the proposed system leverages Cascade R-CNN to detect all vehicles and extract appearance features with a Re-ID module for all cameras. Secondly, in the Multi-Target Single-Camera tracking (MTSC) stage, on the basis of the detected boxes and appearance features, it tracks multiple vehicles to generate candidate trajectories within each single camera with Tracklet-Plane Matching (TPM) tracking algorithm. Finally, in the Inter-Camera Association (ICA) stage, it associates all candidate trajectories between two successive cameras using the established distance matrix, and combines all successively matching results for final submission. The established distance matrix is simply computed by the Re-ID features and refined by the constraints of traveling time, road structures, and traffic rules to accelerate matching time as well as reduce search space. Extensive experiments on the public track3 test set of NVIDIA AI CITY 2021 CHALLENGE demonstrate the effectiveness of our method, which achieves IDF1 of 77.87%.

关键词： Roads Urban areas Cameras Feature extraction Trajectory Space exploration pattern recognition

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