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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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STDLens: Model Hijacking-resilient Federated Learning for Object Detection

STDLens: Model Hijacking-resilient Federated Learning for Ob...

引用

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

作者： Chow, Ka-Ho Liu, Ling Wei, Wenqi Ilhan, Fatih Wu, Yanzhao Georgia Instutite Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350301298

Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clients. Despite its advantages, FL is vulnerable to model hijacking. The attacker can control how the object detection system should misbehave by implanting Trojaned gradients using only a small number of compromised clients in the collaborative learning process. This paper introduces STDLens, a principled approach to safeguarding FL against such attacks. We first investigate existing mitigation mechanisms and analyze their failures caused by the inherent errors in spatial clustering analysis on gradients. Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL. We consider three types of adaptive attacks and demonstrate the robustness of STDLens against advanced adversaries. Extensive experiments show that STDLens can protect FL against different model hijacking attacks and outperform existing methods in identifying and removing Trojaned gradients with significantly higher precision and much lower false-positive rates. The source code is available at https: //***/git-disl/STDLens.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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Deep Global Registration

Deep Global Registration

引用

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

作者： Choy, Christopher Dong, Wei Koltun, Vladlen Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Intel Labs Hillsboro OR USA

ISBN: (纸本)9781728171685

We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estimation, and a robust gradient-based SE(3) optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data.

关键词： computer vision

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Unsupervised Intrinsic Image Decomposition with LiDAR Intensity

Unsupervised Intrinsic Image Decomposition with LiDAR Intens...

引用

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

作者： Sato, Shogo Yao, Yasuhiro Yoshida, Taiga Kaneko, Takuhiro Ando, Shingo Shimamura, Jun INTT Human Informat Labs Tokyo Japan NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9798350301298

Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods.

关键词： Physics-based vision and shape-from-X

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Efficient On-device Training via Gradient Filtering

Efficient On-device Training via Gradient Filtering

引用

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

作者： Yang, Yuedong Li, Guihong Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350301298

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19x speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy;over 20x speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.

关键词： Efficient and scalable vision

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Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective

Learning Distortion Invariant Representation for Image Resto...

引用

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

作者： Li, Xin Li, Bingchen Jin, Xin Lan, Cuiling Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Eastern Inst Adv Study Ningbo Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtual model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the generalization capability of our DIL on unseen distortion types and degrees. Our code will be available at https://***/lixinustc/Causal-IR-DIL.

关键词： Low-level vision

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Robust 3D Shape Classification via Non-local Graph Attention Network

Robust 3D Shape Classification via Non-local Graph Attention...

引用

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

作者： Qin, Shengwei Li, Zhong Liu, Ligang Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou Peoples R China Huzhou Univ Sch Informat Huzhou Peoples R China Zhejiang Sci Tech Univ Sch Sci Hangzhou Zhejiang Peoples R China Univ Sci & Technol China Sch Math Sci Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

We introduce a non-local graph attention network (NL-GAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods.

关键词： detection recognition: Categorization retrieval

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6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation

6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose E...

引用

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

作者： Xu, Li Qui, Haoxuan Cai, Yujun Liu, Jun Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353006

Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds. Meanwhile, diffusion models have shown appealing performance in generating high-quality images from random noise with high indeterminacy through step-by-step denoising. In-spired by their denoising capability, we propose a novel diffusion-based framework (6D-Diff) to handle the noise and indeterminacy in object pose estimation for better performance. In our framework, to establish accurate 2D-3D correspondence, we formulate 2D keypoints detection as a reverse diffusion (denoising) process. To facilitate such a denoising process, we design a Mixture-of-Cauchy-based forward diffusion process and condition the reverse process on the object appearance features. Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of our framework.

关键词： Object recognition

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Label-Free Liver Tumor Segmentation

Label-Free Liver Tumor Segmentation

引用

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

作者： Hu, Qixin Chen, Yixiong Xiao, Junfei Sun, Shuwen Chen, Jieneng Yuille, Alan Zhou, Zongwei Huazhong Univ Sci & Technol Wuhan Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Johns Hopkins Univ Baltimore MD USA Nanjing Med Univ Affiliated Hosp 1 Nanjing Peoples R China

ISBN: (纸本)9798350301298

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors;(II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors-this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.

关键词： cell microscopy Medical and biological vision

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Learning Customized Visual Models with Retrieval-Augmented Knowledge

Learning Customized Visual Models with Retrieval-Augmented K...

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

作者： Liu, Haotian Son, Kilho Yang, Jianwei Liu, Ce Gao, Jianfeng Lee, Yong Jae Li, Chunyuan Univ Wisconsin Madison Madison WI 53706 USA Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure broad concept coverage, followed by expensive pre-training to feed all the knowledge into model weights. Alternatively, we propose REACT, REtrieval-Augmented CusTomization, a framework to acquire the relevant web knowledge to build customized visual models for target domains. We retrieve the most relevant image-text pairs (similar to 3% of CLIP pre-training data) from the web-scale database as external knowledge and propose to customize the model by only training new modularized blocks while freezing all the original weights. The effectiveness of REACT is demonstrated via extensive experiments on classification, retrieval, detection and segmentation tasks, including zero, few, and full-shot settings. Particularly, on the zero-shot classification task, compared with CLIP, it achieves up to 5.4% improvement on ImageNet and 3.7% on the ELEVATER benchmark (20 datasets).

关键词： and reasoning language vision

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Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark

Visibility Constrained Wide-band Illumination Spectrum Desig...

引用

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

作者： Niu, Muyao Li, Zhuoxiao Zhong, Zhihang Zheng, Yinqiang Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350301298

Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://***/MyNiuuu/VCSD.

关键词： Physics-based vision and shape-from-X

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