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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Privacy Leakage of Adversarial Training Models in Federated ...

引用

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

作者： Zhang, Jingyang Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://***/zjysteven/PrivayAttack_AT_FL.

关键词： Training Deep learning Privacy computer vision conferences Neural networks Collaborative work

来源：评论

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Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data

Variational Autoencoders for Generating Hyperspectral Imagin...

引用

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

作者： Phillips, Tessa Abdulla, Waleed Univ Auckland Auckland New Zealand

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar concentrations. Previous work in this area has used a minimal number of honey types, as sample preparation and data capture is a time-consuming process. This paper develops a new approach using variational autoencoders (VAEs) for generating adulterated honey data for unseen honey types. The results show that the binary adulteration detector can achieve on average 81.3% accuracy on unseen honey types by adding the generated data to the existing training data. Without including the generated data while training, the classifier can only achieve 44% on unseen honey types.

关键词： Training computer vision conferences Training data Machine learning Detectors pattern recognition

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ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Multi-Task Learning Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

引用

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

作者： Kollias, Dimitrios Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with ieee International conference on computer vision and pattern recognition (CVPR), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, ieee FG 2020 and ieee CVPR 2017 conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) MultiTask-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present both the baseline systems and the top performing teams' per Challenge. Finally we illustrate the obtained results of the baseline systems and of all participating teams.

关键词： Measurement computer vision Databases conferences Estimation Benchmark testing Multitasking

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Finding Lottery Tickets in vision Models via Data-driven Spectral Foresight Pruning

Finding Lottery Tickets in Vision Models via Data-driven Spe...

引用

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

作者： Iurada, Leonardo Ciccone, Marco Tommasi, Tatiana Politecn Torino Turin Italy

ISBN: (纸本)9798350353006

Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm that leverages the Neural Tangent Kernel (NTK) theory to align the training dynamics of the sparse network with that of the dense one. Specifically, we show how the usually neglected data-dependent component in the NTK's spectrum can be taken into account by providing an analytical upper bound to the NTK's trace obtained by decomposing neural networks into individual paths. This leads to our Path eXclusion (PX), a foresight pruning method designed to preserve the parameters that mostly influence the NTK's trace. PX is able to find lottery tickets (i.e. good paths) even at high sparsity levels and largely reduces the need for additional training. When applied to pre-trained models it extracts subnetworks directly usable for several downstream tasks, resulting in performance comparable to those of the dense counterpart but with substantial cost and computational savings. Code available at: https://***/iurada/px-ntk-pruning

关键词： Efficient computer vision Neural Network Pruning Neural Tangent Kernel Pruning-at-Initialization

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Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features

Back to 3D: Few-Shot 3D Keypoint Detection with Back-Project...

引用

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

作者： Wimmer, Thomas Wonka, Peter Ovsjanikov, Maks Ecole Polytech LIX Palaiseau France Tech Univ Munich Munich Germany KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350353013;9798350353006

With the immense growth of dataset sizes and computing resources in recent years, so-called foundation models have become popular in NLP and vision tasks. In this work, we propose to explore foundation models for the task of keypoint detection on 3D shapes. A unique characteristic of keypoint detection is that it requires semantic and geometric awareness while demanding high localization accuracy. To address this problem, we propose, first, to back-project features from large pre-trained 2D vision models onto 3D shapes and employ them for this task. We show that we obtain robust 3D features that contain rich semantic information and analyze multiple candidate features stemming from different 2D foundation models. Second, we employ a keypoint candidate optimization module which aims to match the average observed distribution of keypoints on the shape and is guided by the back-projected features. The resulting approach achieves a new state of the art for few-shot keypoint detection on the KeyPointNet dataset, almost doubling the performance of the previous best methods.

关键词： 3D vision Foundation Models Keypoint Detection Shape Analysis

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Stronger, Fewer, & Superior: Harnessing vision Foundation Models for Domain Generalized Semantic Segmentation

Stronger, Fewer, & Superior: Harnessing Vision Foundation Mo...

引用

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

作者： Wei, Zhixiang Chen, Lin Jin, Yi Ma, Xiaoxiao Liu, Tianle Ling, Pengyang Wang, Ben Chen, Huaian Zheng, Jinjin Univ Sci & Technol China Hefei Peoples R China Shanghai Ai Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

In this paper, we first assess and harness various vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely "Rein", to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens, each linked to distinct instances, Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters, Rein efficiently fine-tunes VFMs for DGSS tasks, surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably, with just an extra 1% of trainable parameters within the frozen backbone, Rein achieves a mIoU of 78.4% on the Cityscapes, without accessing any real urban-scene datasets. Code is available at https://***/w1oves/***.

关键词： Domain Generalized Semantic Segmentation parameter-efficient fine-tuning Semantic Segmentation vision Foundation Models

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ZeroShape: Regression-based Zero-shot Shape Reconstruction

ZeroShape: Regression-based Zero-shot Shape Reconstruction

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

作者： Huang, Zixuan Stojanov, Stefan Thai, Anh Jampani, Varun Rehg, James M. Univ Illinois Champaign IL 61820 USA Georgia Inst Technol Atlanta GA 30332 USA Stabil AI London England

ISBN: (纸本)9798350353006

We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape. Such regression methods possess much higher computational efficiency than generative methods. This raises a natural question: is generative modeling necessary for high performance, or conversely, are regression-based approaches still competitive? To answer this, we design a strong regression-based model, called ZeroShape, based on the converging findings in this field and a novel insight. We also curate a large real-world evaluation benchmark, with objects from three different real-world 3D datasets. This evaluation benchmark is more diverse and an order of magnitude larger than what prior works use to quantitatively evaluate their models, aiming at reducing the evaluation variance in our field. We show that ZeroShape not only achieves superior performance over state-of-the-art methods, but also demonstrates significantly higher computational and data efficiency.(1)

关键词： 3D generation 3D reconstruction 3D vision computer vision Deep Learning

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Test-Time Adaptation for Depth Completion

Test-Time Adaptation for Depth Completion

引用

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

作者： Park, Hyoungseob Gupta, Anjali Wong, Alex Yale Vision Lab New Haven CT 06501 USA

ISBN: (纸本)9798350353006

It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap, such as domain adaptation (DA), may require the source data on which the model was trained (often not available), while others, i.e., source-free DA, require many passes through the testing data. We propose an online test-time adaptation method for depth completion, the task of inferring a dense depth map from a single image and associated sparse depth map, that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image, we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%. Code available at ***/seobbro/TTA-depth-completion.

关键词： 3D computer vision Depth completion Depth estimation Multi-modal fusion Test time adaptation

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Distilling vision-Language Models on Millions of Videos

Distilling Vision-Language Models on Millions of Videos

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

作者： Zhao, Yue Zhao, Long Zhou, Xingyi Wu, Jialin Chu, Chun-Te Mia, Hui Schroff, Florian Adam, Hartwig Liu, Ting Gong, Boqing Krahenbuhl, Philipp Yuan, Liangzhe Google Res Mountain View CA 94043 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350353006

The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human- curated video-text data available. We thus resort to fine-tuning a video-language model from a strong image-language baseline with synthesized instructional data. The resulting video model by video-instruction-tuning (VIIT) is then used to auto-label millions of videos to generate high-quality captions. We show the adapted video-language model performs well on a wide range of video-language benchmarks. For instance, it surpasses the best prior result on open-ended NExT-QA by 2.8%. Besides, our model generates detailed descriptions for previously unseen videos, which provide better textual supervision than existing methods. Experiments show that a video-language dual-encoder model contrastively trained on these auto-generated captions is 3.8% better than the strongest baseline that also leverages vision-language models. Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%. As a side product, we generate the largest video capation dataset to date.

关键词： Video analysis

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LQMFormer: Language-aware Query Mask Transformer for Referring Image Segmentation

LQMFormer: Language-aware Query Mask Transformer for Referri...

引用

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

作者： Shah, Nisarg A. Vibashan, V. S. Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

Referring Image Segmentation (RIS) aims to segment objects from an image based on a language description. Recent advancements have introduced transformer-based methods that leverage cross-modal dependencies, significantly enhancing performance in referring segmentation tasks. These methods are designed such that each query predicts different masks. However, RIS inherently requires a single-mask prediction, leading to a phenomenon known as Query Collapse, where all queries yield the same mask prediction. This reduces the generalization capability of the RIS model for complex or novel scenarios. To address this issue, we propose a Multi-modal Query Feature Fusion technique, characterized by two innovative designs: (1) Gaussian enhanced Multi-Modal Fusion, a novel visual grounding mechanism that enhances overall representation by extracting rich local visual information and global visual-linguistic relationships, and (2) A Dynamic Query Module that produces a diverse set of queries through a scoring network where the network selectively focuses on queries for objects referred to in the language description. Moreover, we show that including an auxiliary loss to increase the distance between mask representations of different queries further enhances performance and mitigates query collapse. Extensive experiments conducted on four benchmark datasets validate the effectiveness of our framework.

关键词： Image segmentation Multimodal Transformer vision-Language

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