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Convolutional neural network architecture for geometric matching 30

Convolutional neural network architecture for geometric matc...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rocco, Ignacio Arandjelovic, Relja Sivic, Josef DI ENS Paris France INRIA Villeneuve Dascq France CIIRC Prague Czech Republic PSL Res Univ ENS Dept Informat CNRS F-75005 Paris France Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic DeepMind London England

ISBN: (纸本)9781538604571

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous in-lier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.

关键词： Correlation Feature extraction computer architecture Estimation Geometry Robustness Neural networks

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Intrinsic Physical Concepts Discovery with Object-Centric Predictive Models

Intrinsic Physical Concepts Discovery with Object-Centric Pr...

引用

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

作者： Tang, Qu Zhu, Xiangyu Lei, Zhen Zhang, Zhaoxiang Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

The ability to discover abstract physical concepts and understand how they work in the world through observing lies at the core of human intelligence. The acquisition of this ability is based on compositionally perceiving the environment in terms of objects and relations in an unsupervised manner. Recent approaches learn object-centric representations and capture visually observable concepts of objects, e.g., shape, size, and location. In this paper, we take a step forward and try to discover and represent intrinsic physical concepts such as mass and charge. We introduce the PHYsical Concepts Inference NEtwork (PHYCINE), a system that infers physical concepts in different abstract levels without supervision. The key insights underlining PHYCINE are two-fold, commonsense knowledge emerges with prediction, and physical concepts of different abstract levels should be reasoned in a bottom-up fashion. Empirical evaluation demonstrates that variables inferred by our system work in accordance with the properties of the corresponding physical concepts. We also show that object representations containing the discovered physical concepts variables could help achieve better performance in causal reasoning tasks, i.e., ComPhy.

关键词： vision applications and systems

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Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation

Uncertainty-Aware Adaptation for Self-Supervised 3D Human Po...

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

作者： Kundu, Jogendra Nath Seth, Siddharth Pradyumna, Y. M. Jampani, Varun Chakraborty, Anirban Babu, R. Venkatesh Indian Inst Sci Bangalore Karnataka India Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard unfamiliar out-of-distribution data. To this end, we cast the 3D human pose learning as an unsupervised domain adaptation problem. We introduce MRP-Net(1) that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations;a) model free joint localization and b) model-based parametric regression. Such a design allows us to derive suitable measures to quantify prediction uncertainty at both pose and joint level granularity. While supervising only on labeled synthetic samples, the adaptation process aims to minimize the uncertainty for the unlabeled target images while maximizing the same for an extreme out-of-distribution dataset (backgrounds). Alongside synthetic-to-real 3D pose adaptation, the joint-uncertainties allow expanding the adaptation to work on in-the-wild images even in the presence of occlusion and truncation scenarios. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.

关键词： Location awareness Adaptation models Solid modeling Three-dimensional displays Uncertainty Head Face recognition

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Edge-Labeling Graph Neural Network for Few-shot Learning 32

Edge-Labeling Graph Neural Network for Few-shot Learning

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Jongmin Kim, Taesup Kim, Sungwoong Yoo, Chang D. Korea Adv Inst Sci & Technol Daejeon South Korea Univ Montreal MILA Montreal PQ Canada Kakao Brain Jeju City South Korea

ISBN: (纸本)9781728132938

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on the node-labeling framework, which implicitly models the intra-cluster similarity and the inter-cluster dissimilarity. In contrast, the proposed EGNN learns to predict the edge-labels rather than the node-labels on the graph that enables the evolution of an explicit clustering by iteratively updating the edge-labels with direct exploitation of both intra-cluster similarity and the inter-cluster dissimilarity. It is also well suited for performing on various numbers of classes without retraining, and can be easily extended to perform a transductive inference. The parameters of the EGNN are learned by episodic training with an edge-labeling loss to obtain a well-generalizable model for unseen low-data problem. On both of the supervised and semi-supervised few-shot image classification tasks with two benchmark datasets, the proposed EGNN significantly improves the performances over the existing GNNs.

关键词： Categorization Deep Learning Grouping a recognition: Detection Representation Learning Retrieval Segmentation

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Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task

Rope3D: The Roadside Perception Dataset for Autonomous Drivi...

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

作者： Ye, Xiaoqing Shu, Mao Li, Hanyu Shi, Yifeng Li, Yingying Wang, Guangjie Tan, Xiao Ding, Errui Baidu Inc Beijing Peoples R China China Univ Min & Technol Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Concurrent perception datasets for autonomous driving are mainly limited to frontal view with sensors mounted on the vehicle. None of them is designed for the overlooked roadside perception tasks. On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system. To accelerate the progress of roadside perception, we present the first high-diversity challenging Roadside Perception 3D dataset- Rope3D from a novel view. The dataset consists of 50k images and over 1.5M 3D objects in various scenes, which are captured under different settings including various cameras with ambiguous mounting positions, camera specifications, viewpoints, and different environmental conditions. We conduct strict 2D-3D joint annotation and comprehensive data analysis, as well as set up a new 3D roadside perception benchmark with metrics and evaluation devkit. Furthermore, we tailor the existing frontal-view monocular 3D object detection approaches and propose to leverage the geometry constraint to solve the inherent ambiguities caused by various sensors, viewpoints. Our dataset is available on hups://***/rope.

关键词： Measurement Three-dimensional displays Annotations Roads Object detection Cameras pattern recognition

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Action snippets: How many frames does human action recognition require?

Action snippets: How many frames does human action recogniti...

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ieee conference on computer vision and pattern recognition

作者： Schindler, Konrad van Gool, Luc Swiss Fed Inst Technol BIWI Zurich Switzerland Katholieke Univ Leuven ESAT Leuven Belgium

ISBN: (纸本)9781424422425

Visual recognition of human actions in video clips has been an active field of research in recent years. However, most published methods either analyse an entire video and assign it a single action label, or use relatively large look-ahead to classify each frame. Contrary to these strategies, human vision proves that simple actions can be recognised almost instantaneously. In this paper, we present a system for action recognition from very short sequences ("snippets") of 1-10 frames, and systematically evaluate it on standard data sets. It turns out that even local shape and optic flow for a single frame are enough to achieve approximate to 90% correct recognitions, and snippets of 5-7 frames (0.3-0.5 seconds of video) are enough to achieve a performance similar to the one obtainable with the entire video sequence.

关键词： Image recognition

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Exploring Adversarial Fake Images on Face Manifold

Exploring Adversarial Fake Images on Face Manifold

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

作者： Li, Dongze Wang, Wei Fan, Hongxing Dong, Jing Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China

ISBN: (纸本)9781665445092

Images synthesized by powerful generative adversarial network (GAN) based methods have drawn moral and privacy concerns. Although image forensic models have reached great performance in detecting fake images from real ones, these models can be easily fooled with a simple adversarial attack. But, the noise adding adversarial samples are also arousing suspicion. In this paper, instead of adding adversarial noise, we optimally search adversarial points on face manifold to generate anti forensic fake face images. We iteratively do a gradient-descent with each small step in the latent space of a generative model, e.g. Style-GAN, to find an adversarial latent vector, which is similar to norm-based adversarial attack but in latent space. Then, the generated fake images driven by the adversarial latent vectors with the help of GANs can defeat main-stream forensic models. For examples, they make the accuracy of deepfake detection models based on Xception or EfficientNet drop from over 90% to nearly 0%, meanwhile maintaining high visual quality. In addition, we find manipulating noise vectors n at different levels have different impacts on attack success rate, and the generated adversarial images mainly have changes on facial texture or face attributes.

关键词： Manifolds Visualization Privacy Image forensics Ethics computer vision Face recognition

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Compacting Binary Neural Networks by Sparse Kernel Selection

Compacting Binary Neural Networks by Sparse Kernel Selection

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

作者： Wang, Yikai Huang, Wenbing Dong, Yinpeng Sun, Fuchun Yao, Anbang Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst BNRist Ctr Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Suzhou Peoples R China RealAI Shenzhen Peoples R China Intel Labs China Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning non-repetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full codebook. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets.

关键词： Efficient and scalable vision

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Prompting vision Foundation Models for Pathology Image Analysis

Prompting Vision Foundation Models for Pathology Image Analy...

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

作者： Yin, Chong Liu, Siqi Zhou, Kaiyang Wong, Vincent Wai-Sun Yuen, Pong C. Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Dept Med & Therapeut Hong Kong Peoples R China

ISBN: (纸本)9798350353006

The rapid increase in cases of non-alcoholic fatty liver disease (NAFLD) in recent years has raised significant public concern. Accurately identifying tissue alteration regions is crucial for the diagnosis of NAFLD, but this task presents challenges in pathology image analysis, particularly with small-scale datasets. Recently, the paradigm shift from full fine-tuning to prompting in adapting vision foundation models has offered a new perspective for small-scale data analysis. However, existing prompting methods based on task-agnostic prompts are mainly developed for generic image recognition, which fall short in providing instructive cues for complex pathology images. In this paper, we propose Quantitative Attribute-based Prompting (QAP), a novel prompting method specifically for liver pathology image analysis. QAP is based on two quantitative attributes, namely K-function-based spatial attributes and histogram-based morphological attributes, which are aimed for quantitative assessment of tissue states. Moreover, a conditional prompt generator is designed to turn these instance-specific attributes into visual prompts. Extensive experiments on three diverse tasks demonstrate that our task-specific prompting method achieves better diagnostic performance as well as better interpretability. Code is available at https://***/7LFB/QAP.

关键词： pathology image analysis Prompt quantitative attributes

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When Does Contrastive Visual Representation Learning Work?

When Does Contrastive Visual Representation Learning Work?

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

作者： Cole, Elijah Yang, Xuan Wilber, Kimberly Mac Aodha, Oisin Belongie, Serge CALTECH Pasadena CA 91125 USA Google Mountain View CA 94043 USA Univ Edinburgh Edinburgh Midlothian Scotland Alan Turing Inst London England Univ Copenhagen Copenhagen Denmark

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification. While the particulars of pretraining on ImageNet are now relatively well understood, the field still lacks widely accepted best practices for replicating this success on other datasets. As a first step in this direction, we study contrastive self-supervised learning on four diverse large-scale datasets. By looking through the lenses of data quantity, data domain, data quality, and task granularity, we provide new insights into the necessary conditions for successful self-supervised learning. Our key findings include observations such as: (i) the benefit of additional pretraining data beyond 500k images is modest, (ii) adding pretraining images from another domain does not lead to more general representations, (iii) corrupted pretraining images have a disparate impact on supervised and self-supervised pretraining, and (iv) contrastive learning lags far behind supervised learning on fine-grained visual classification tasks.

关键词： Representation learning Training Visualization Protocols Supervised learning Self-supervised learning computer architecture

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