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

作者： Pang, Jiangmiao Qiu, Linlu Li, Xia Chen, Haofeng Li, Qi Darrell, Trevor Yu, Fisher Zhejiang Univ Hangzhou Zhejiang Peoples R China Georgia Inst Technol Atlanta GA 30332 USA Swiss Fed Inst Technol Zurich Switzerland Stanford Univ Stanford CA 94305 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning. We can directly combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacement regression or motion priors. We also find that the resulting distinctive feature space admits a simple nearest neighbor search at the inference time. Despite its simplicity, QDTrack outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks. It achieves 68.7 MOTA at 20.3 FPS on MOT17 without using external training data. Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.

关键词： Training Image segmentation computer vision Codes Training data Detectors Nearest neighbor methods

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Soteria: Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Soteria: Provable Defense against Privacy Leakage in Federat...

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

作者： Sun, Jingwei Li, Ang Wang, Binghui Yang, Huanrui Li, Hai Chen, Yiran Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (纸本)9781665445092

Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works have demonstrated that sharing model updates makes FL vulnerable to inference attack. In this work, we show our key observation that the data representation leakage from gradients is the essential cause of privacy leakage in FL. We also provide an analysis of this observation to explain how the data presentation is leaked. Based on this observation, we propose a defense called Soteria against model inversion attack in FL. The key idea of our defense is learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained. In addition, we derive a certified robustness guarantee to FL and a convergence guarantee to FedAvg, after applying our defense. To evaluate our defense, we conduct experiments on MNIST and CIFAR10 for defending against the DLG attack and GS attack. Without sacrificing accuracy, the results demonstrate that our proposed defense can increase the mean squared error between the reconstructed data and the raw data by as much as 160x for both DLG attack and GS attack, compared with baseline defense methods. Therefore, the privacy of the FL system is significantly improved. Our code can be found at https://***/jeremy313/Soteria.

关键词： Privacy Data privacy computer vision Distance learning Distributed databases Collaborative work Robustness

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Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

Few-Shot Segmentation Without Meta-Learning: A Good Transduc...

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

作者： Boudiaf, Malik Kervadec, Hoel Masud, Ziko Imtiaz Piantanida, Pablo Ben Ayed, Ismail Dolz, Jose ETS Montreal Montreal PQ Canada Univ Paris Saclay CNRS Cent Supelec Paris France

ISBN: (纸本)9781665445092

We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances-an aspect often overlooked in the literature in favor of the meta-learning paradigm. We introduce a transductive inference for a given query image, leveraging the statistics of its unlabeled pixels, by optimizing a new loss containing three complementary terms: i) the cross-entropy on the labeled support pixels;ii) the Shannon entropy of the posteriors on the unlabeled query-image pixels;and iii) a global KL-divergence regularizer based on the proportion of the predicted foreground. As our inference uses a simple linear classifier of the extracted features, its computational load is comparable to inductive inference and can be used on top of any base training. Foregoing episodic training and using only standard cross-entropy training on the base classes, our inference yields competitive performances on standard benchmarks in the 1-shot scenarios. As the number of available shots increases, the gap in performances widens: on PASCAL-5(i), our method brings about 5% and 6% improvements over the state-of-the-art, in the 5- and 10-shot scenarios, respectively. Furthermore, we introduce a new setting that includes domain shifts, where the base and novel classes are drawn from different datasets. Our method achieves the best performances in this more realistic setting. Our code is freely available online: https://***/mboudiaf/RePRI-for-Few-Shot-Segmentation.

关键词： Training Image segmentation computer vision Codes Benchmark testing Feature extraction Entropy

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Inferring Shared Attention in Social Scene Videos 31

Inferring Shared Attention in Social Scene Videos

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Fan, Lifeng Chen, Yixin Wei, Ping Wang, Wenguan Zhu, Song-Chun Univ Calif Los Angeles Ctr Vis Cognit Learning & Auton Los Angeles CA 90095 USA Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9781538664209

This paper addresses a new problem of inferring shared attention in third-person social scene videos. Shared attention is a phenomenon that two or more individuals simultaneously look at a common target in social scenes. Perceiving and identifying shared attention in videos plays crucial roles in social activities and social scene understanding. We propose a spatial-temporal neural network to detect shared attention intervals in videos and predict shared attention locations in frames. In each video frame, human gaze directions and potential target boxes are two key features for spatially detecting shared attention in the social scene. In temporal domain, a convolutional Long ShortTerm Memory network utilizes the temporal continuity and transition constraints to optimize the predicted shared attention heatmap. We collect a new dataset VideoCoAtt1 from public TV show videos, containing 380 complex video sequences with more than 492,000 frames that include diverse social scenes for shared attention study. Experiments on this dataset show that our model can effectively infer shared attention in videos. We also empirically verify the effectiveness of different components in our model.

关键词： Videos TV Head computer vision Task analysis Motion pictures

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LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search

LightTrack: Finding Lightweight Neural Networks for Object T...

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

作者： Yan, Bin Peng, Houwen Wu, Kan Wang, Dong Fu, Jianlong Lu, Huchuan Microsoft Res Asia Beijing Peoples R China Dalian Univ Technol Dalian Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployments in resource-constrained applications. In this work, we present LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers. Comprehensive experiments show that our LightTrack is effective. It can find trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ [30] and Ocean [56], while using much fewer model Flops and parameters. Moreover, when deployed on resource-constrained mobile chipsets, the discovered trackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack runs 12x faster than Ocean, while using 13x fewer parameters and 38x fewer Flops. Such improvements might narrow the gap between academic models and industrial deployments in object tracking task. LightTrack is released at here.

关键词： computer vision Oceans Neural networks Graphics processing units computer architecture Search problems Real-time systems

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Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Neural Parts: Learning Expressive 3D Shape Abstractions with...

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

作者： Paschalidou, Despoina Katharopoulos, Angelos Geiger, Andreas Fidler, Sanja Max Planck Inst Intelligent Syst Tubingen Tubingen Germany Univ Tubingen Tubingen Germany Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Max Planck ETH Ctr Learning Syst Zurich Switzerland NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision. Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Shape Computational modeling Neural networks

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Simulating Unknown Target Models for Query-Efficient Black-box Attacks

Simulating Unknown Target Models for Query-Efficient Black-b...

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

作者： Ma, Chen Chen, Li Yong, Jun-Hai Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute model to counterfeit the functionality of the target model. However, the training requires querying the target model. Consequently, the query complexity remains high, and such attacks can be defended easily. This study aims to train a generalized substitute model called "Simulator", which can mimic the functionality of any unknown target model. To this end, we build the training data with the form of multiple tasks by collecting query sequences generated during the attacks of various existing networks. The learning process uses a mean square error-based knowledge-distillation loss in the meta-learning to minimize the difference between the Simulator and the sampled networks. The meta-gradients of this loss are then computed and accumulated from multiple tasks to update the Simulator and subsequently improve generalization. When attacking a target model that is unseen in training, the trained Simulator can accurately simulate its functionality using its limited feedback. As a result, a large fraction of queries can be transferred to the Simulator, thereby reducing query complexity. Results of the comprehensive experiments conducted using the CIFAR-10, CIFAR-100, and TinyImageNet datasets demonstrate that the proposed approach reduces query complexity by several orders of magnitude compared to the baseline method. The implementation source code is released online(1).

关键词： Training Deep learning computer vision Codes Computational modeling Training data Complexity theory

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SGTR: End-to-end Scene Graph Generation with Transformer

SGTR: End-to-end Scene Graph Generation with Transformer

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

作者： Li, Rongjie Zhang, Songyang He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up two-stage or a point-based one-stage approach, which often suffers from high time complexity or sub-optimal designs. In this work, wc propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To solve the problem, we develop a transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets. In particular, we develop a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on two challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. We hope our model can serve as a strong baseline for the Transformer-based scene graph generation.(1)

关键词： Visualization Image analysis Transformers pattern recognition Decoding Bipartite graph Proposals

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Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks

Vox2Cortex: Fast Explicit Reconstruction of Cortical Surface...

引用

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

作者： Bongratz, Fabian Rickmann, Anne-Marie Polsterl, Sebastian Wachinger, Christian Tech Univ Munich Munich Germany Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. Although traditional and deep learning-based algorithmic pipelines exist for this purpose, they have two major drawbacks: lengthy runtimes of multiple hours (traditional) or intricate post-processing, such as mesh extraction and topology correction (deep learning-based). In this work, we address both of these issues and propose Vox2Cortex, a deep learning-based algorithm that directly yields topologically correct, three-dimensional meshes of the boundaries of the cortex. Vox2Cortex leverages convolutional and graph convolutional neural networks to deform an initial template to the densely folded geometry of the cortex represented by an input MRI scan. We show in extensive experiments on three brain MRI datasets that our meshes are as accurate as the ones reconstructed by state-of-the-art methods in the field, without the need for time- and resource-intensive post-processing. To accurately reconstruct the tightly folded cortex, we work with meshes containing about 168,000 vertices at test time, scaling deep explicit reconstruction methods to a new level.

关键词： Geometry Deep learning Surface reconstruction Three-dimensional displays Magnetic resonance imaging Surface morphology computer architecture

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Text-image Alignment for Diffusion-based Perception

Text-image Alignment for Diffusion-based Perception

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

作者： Kondapanenil, Neehar Marksl, Markus Knott, Manuel Guimaraes, Rogerio Perona, Pietro CALTECH Pasadena CA 91125 USA Swiss Data Sci Ctr Empa ETH Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: ***/TADP/Code page: ***/damaggu/TADP

关键词： ADE20K Alignment Cityscapes Cross-Attention Cross-Domain Depth Estimation Diffusion Domain Shift Generative Models Model Personalization NYUv2 Object Detection Segmentation vision-Language Models

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