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A Second-Order Approach to Learning with Instance-Dependent Label Noise

A Second-Order Approach to Learning with Instance-Dependent ...

引用

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

作者： Zhu, Zhaowei Liu, Tongliang Liu, Yang Univ Calif Santa Cruz Comp Sci & Engn Santa Cruz CA 95064 USA Univ Sydney Trustworthy Machine Learning Lab Sydney NSW Australia

ISBN: (纸本)9781665445092

The presence of label noise often misleads the training of deep neural networks. Departing from the recent literature which largely assumes the label noise rate is only determined by the true label class, the errors in human-annotated labels are more likely to be dependent on the difficulty levels of tasks, resulting in settings with instance-dependent label noise. We first provide evidences that the heterogeneous instance-dependent label noise is effectively downweighting the examples with higher noise rates in a non-uniform way and thus causes imbalances, rendering the strategy of directly applying methods for class-dependent label noise questionable. Built on a recent work peer loss [24], we then propose and study the potentials of a second-order approach that leverages the estimation of several covariance terms defined between the instance-dependent noise rates and the Bayes optimal label. We show that this set of second-order statistics successfully captures the induced imbalances. We further proceed to show that with the help of the estimated second-order statistics, we identify a new loss function whose expected risk of a classifier under instance-dependent label noise is equivalent to a new problem with only class-dependent label noise. This fact allows us to apply existing solutions to handle this better-studied setting. We provide an efficient procedure to estimate these second-order statistics without accessing either ground truth labels or prior knowledge of the noise rates. Experiments on CIFAR10 and CIFAR100 with synthetic instance-dependent label noise and Clothing1M with real-world human label noise verify our approach.

关键词： Training Deep learning computer vision Estimation Rendering (computer graphics) pattern recognition Task analysis

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Network Pruning via Performance Maximization

Network Pruning via Performance Maximization

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

作者： Gao, Shangqian Huang, Feihu Cai, Weidong Huang, Heng Univ Pittsburgh Elect & Comp Engn Dept Pittsburgh PA 15260 USA Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia JD Finance Amer Corp Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Channel pruning is a class of powerful methods for model compression. When pruning a neural network, it's ideal to obtain a sub-network with higher accuracy. However, a subnetwork does not necessarily have high accuracy with low classification loss (loss-metric mismatch). In the paper, we first consider the loss-metric mismatch problem for pruning and propose a novel channel pruning method for Convolutional Neural Networks (CNNs) by directly maximizing the performance (i.e., accuracy) of sub-networks. Specifically, we train a stand-alone neural network to predict sub-networks' performance and then maximize the output of the network as a proxy of accuracy to guide pruning. Training such a performance prediction network efficiently is not an easy task, and it may potentially suffer from the problem of catastrophic forgetting and the imbalance distribution of sub-networks. To deal with this challenge, we introduce a corresponding episodic memory to update and collect sub-networks during the pruning process. In the experiment section, we further demonstrate that the gradients from the performance prediction network and the classification loss have different directions. Extensive experimental results show that the proposed method can achieve state-of-the-art performance with ResNet, MobileNetV2, and ShuffleNetV2+ on ImageNet and CIFAR-10.

关键词： Training computer vision Neural networks Memory modules pattern recognition Convolutional neural networks Task analysis

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Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond

Towards Robust Rain Removal Against Adversarial Attacks: A C...

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

作者： Yu, Yi Yang, Wenhan Tan, Yap-Peng Kot, Alex C. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Nanyang Technol Univ Interdisciplinary Grad Programme ROSE Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Rain removal aims to remove rain streaks from images/videos and reduce the disruptive effects caused by rain. It not only enhances image/video visibility but also allows many computer vision algorithms to function properly. This paper makes the first attempt to conduct a comprehensive study on the robustness of deep learning-based rain removal methods against adversarial attacks. Our study shows that, when the image/video is highly degraded, rain removal methods are more vulnerable to the adversarial attacks as small distortions/perturbations become less noticeable or detectable. In this paper, we first present a comprehensive empirical evaluation of various methods at different levels of attacks and with various losses/targets to generate the perturbations from the perspective of human perception and machine analysis tasks. A systematic evaluation of key modules in existing methods is performed in terms of their robustness against adversarial attacks. From the insights of our analysis, we construct a more robust deraining method by integrating these effective modules. Finally, we examine various types of adversarial attacks that are specific to deraining problems and their effects on both human and machine vision tasks, including 1) rain region attacks, adding perturbations only in the rain regions to make the perturbations in the attacked rain images less visible;2) object-sensitive attacks, adding perturbations only in regions near the given objects. Code is available at https://***/yuyidad/Robust_Rain_Removal.

关键词： computer vision Rain Systematics Codes Perturbation methods Machine vision Benchmark testing

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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

作者： Ventura, Jonathan Kukelova, Zuzana Sattler, Torsten Barath, Daniel Cal Poly Dept Comp Sci & Software Engn San Luis Obispo CA USA Czech Tech Univ Visual Recognit Grp Fac Elect Engn Prague Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Swiss Fed Inst Technol Dept Comp Sci Comp Vision & Geometry Grp Zurich Switzerland

ISBN: (纸本)9798350353006

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https: //***/danini/absolute-pose-from-orientedand-scaled-features.

关键词： absolute pose computer vision geometric vision image-based localization minimal solver

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MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

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

作者： Qiu, Zhaofan Yao, Ting Ngo, Chong-Wah Mei, Tao JD Explore Acad Beijing Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MSA) or multi-layer perceptrons (MLPs), become more and more popular. Nevertheless, it is not trivial when utilizing these newly-minted networks for video recognition due to the large variations and complexities in video data. In this paper, we present MLP-3D networks, a novel MLP-like 3D architecture for video recognition. Specifically, the architecture consists of MLP-3D blocks, where each block contains one MLP applied across tokens (i.e., token-mixing MLP) and one MLP applied independently to each token (i.e., channel MLP). By deriving the novel grouped time mixing (GTM) operations, we equip the basic token-mixing MLP with the ability of temporal modeling. GTM divides the input tokens into several temporal groups and linearly maps the tokens in each group with the shared projection matrix. Furthermore, we devise several variants of GTM with different grouping strategies, and compose each variant in different blocks of MLP-3D network by greedy architecture search. Without the dependence on convolutions or attention mechanisms, our MLP-3D networks achieves 68.5%/81.4% top-1 accuracy on Something-Something V2 and Kinetics-400 datasets, respectively. Despite with fewer computations, the results are comparable to state-of-the-art widely-used 3D CNNs and video transformers.

关键词： Visualization computer vision Three-dimensional displays computer architecture Transformers pattern recognition Complexity theory

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ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations

ImFace: A Nonlinear 3D Morphable Face Model with Implicit Ne...

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

作者： Zheng, Mingwu Yang, Hongyu Huang, Di Chen, Liming Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Ecole Cent Lyon LIRIS Lyon France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Precise representations of 3D faces are beneficial to various computer vision and graphics applications. Due to the data discretization and model linearity, however, it remains challenging to capture accurate identity and expression clues in current studies. This paper presents a novel 3D morphable face model, namely ImFace, to learn a nonlinear and continuous space with implicit neural representations. It builds two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, and designs an improved learning strategy to extend embeddings of expressions w allow more diverse changes. We further introduce a Neural Blend-Field to learn sophisticated details by adaptively blending a series of local fields. In addition to ImFace, an effective preprocessing pipeline is proposed to address the issue of watertight input requirement in implicit representations, enabling them to work with common facial surfaces for the first time. Extensive experiments are performed to demonstrate the superiority of ImFace.

关键词： Solid modeling computer vision Three-dimensional displays Shape Computational modeling Pipelines Linearity

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MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection

MSTR: Multi-Scale Transformer for End-to-End Human-Object In...

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

作者： Kim, Bumsoo Mun, Jonghwan On, Kyoung-Woon Shin, Minchul Lee, Junhyun Kim, Eun-Sol LG AI Res Geumsan South Korea Kakao Brain Seongnam South Korea Korea Univ Seoul South Korea Hanyang Univ Dept Comp Sci Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction (HOI) detection is the task of identifying a set of (human, object, interaction) triplets from an image. Recent work proposed transformer encoder-decoder architectures that successfully eliminated the need for many hand-designed components in HOI detection through end-to-end training. However, they are limited to single-scale feature resolution, providing suboptimal performance in scenes containing humans, objects, and their interactions with vastly different scales and distances. To tackle this problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered by two novel HOIaware deformable attention modules called Dual-Entity attention and Entity-conditioned Context attention. While existing deformable attention comes at a huge cost in HOI detection performance, our proposed attention modules of MSTR learn to effectively attend to sampling points that are essential to identify interactions. In experiments, we achieve the new state-of-the-art performance on two HOI detection benchmarks.

关键词： Training Image resolution Semantics Detectors computer architecture Benchmark testing Transformers

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Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction

Spatio-Temporal Gating-Adjacency GCN for Human Motion Predic...

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

作者： Zhong, Chongyang Hu, Lei Zhang, Zihao Ye, Yongjing Xia, Shihong Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Predicting future motion based on historical motion sequence is a fundamental problem in computer vision, and it has wide applications in autonomous driving and robotics. Some recent works have shown that Graph Convolutional Networks(GCN) are instrumental in modeling the relationship between different joints. However, considering the variants and diverse action types in human motion data, the cross-dependency of the spatio-temporal relationships will be difficult to depict due to the decoupled modeling strategy, which may also exacerbate the problem of insufficient generalization. Therefore, we propose the Spatio-Temporal Gating-Adjacency GCN(GAGCN) to learn the complex spatio-temporal dependencies over diverse action types. Specifically, we adopt gating networks to enhance the generalization of GCN via the trainable adaptive adjacency matrix obtained by blending the candidate spatio-temporal adjacency matrices. Moreover, GAGCN addresses the cross-dependency of space and time by balancing the weights of spatio-temporal modeling and fusing the decoupled spatio-temporal features. Extensive experiments on Human 3.6M, AMASS, and 3DPW demonstrate that GAGCN achieves state-of-the-art performance in both short-term and long-term predictions.

关键词： Graphics computer vision Adaptation models Adaptive systems Tracking Computational modeling Instruments

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Beyond Image Super-Resolution for Image recognition with Task-Driven Perceptual Loss

Beyond Image Super-Resolution for Image Recognition with Tas...

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

作者： Kim, Jaeha Oh, Junghun Lee, Kyoung Mu Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However, due to the ill-posed property of SR, it is challenging for typical SR methods to restore task-relevant high-frequency contents, which may dilute the advantage of utilizing the SR method. Therefore, in this paper, we propose Super-Resolution for Image recognition (SR4IR) that effectively guides the generation of SR images beneficial to achieving satisfactory image recognition performance when processing LR images. The critical component of our SR4IR is the task-driven perceptual (TDP) loss that enables the SR network to acquire task-specific knowledge from a network tailored for a specific task. Moreover, we propose a cross-quality patch mix and an alternate training framework that significantly enhances the efficacy of the TDP loss by addressing potential problems when employing the TDP loss. Through extensive experiments, we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task, including semantic segmentation, object detection, and image classification. The implementation code is available at https://***/JaehaKim97/SR4IR.

关键词： Low-level vision Perceptual loss Super-resolution Task-aware restoration

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BACON: Band-limited Coordinate Networks for Multiscale Scene Representation

BACON: Band-limited Coordinate Networks for Multiscale Scene...

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

作者： Lindell, David B. Van Veen, Dave Park, Jeong Joon Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Coordinate-based networks have emerged as a powerful tool for 3D representation and scene reconstruction. These networks are trained to map continuous input coordinates to the value of a signal at each point. Still, current architectures are black boxes: their spectral characteristics cannot be easily analyzed, and their behavior at unsupervised points is difficult to predict. Moreover, these networks are typically trained to represent a signal at a single scale, so naive downsampling or upsampling results in artifacts. We introduce band-limited coordinate networks (BACON), a network architecture with an analytical Fourier spectrum. BACON has constrained behavior at unsupervised points, can be designed based on the spectral characteristics of the represented signal, and can represent signals at multiple scales without per-scale supervision. We demonstrate BACON for multiscale neural representation of images, radiance fields, and 3D scenes using signed distance functions and show that it outperforms conventional single-scale co-ordinate networks in terms of interpretability and quality.

关键词： Time-frequency analysis Three-dimensional displays Fitting Bandwidth Network architecture Rendering (computer graphics) Behavioral sciences

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