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StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place recognition

StructVPR: Distill Structural Knowledge with Weighting Sampl...

引用

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

作者： Shen, Yanqing Zhou, Sanping Fu, Jingwen Wang, Ruotong Chen, Shitao Zheng, Nanning Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intel Natl Engn Res Ctr Visual Informat & Applicat Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9798350301298

Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost.

关键词： detection recognition: Categorization retrieval

来源：评论

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Zero-shot Referring Image Segmentation with Global-Local Context Features

Zero-shot Referring Image Segmentation with Global-Local Con...

引用

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

作者： Yu, Seonghoon Seo, Paul Hongsuck Son, Jeany GIST AI Grad Sch Gwangju South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP. In order to obtain segmentation masks grounded to the input text, we propose a mask-guided visual encoder that captures global and local contextual information of an input image. By utilizing instance masks obtained from off-the-shelf mask proposal techniques, our method is able to segment fine-detailed instance-level groundings. We also introduce a global-local text encoder where the global feature captures complex sentence-level semantics of the entire input expression while the local feature focuses on the target noun phrase extracted by a dependency parser. In our experiments, the proposed method outperforms several zero-shot baselines of the task and even the weakly supervised referring expression segmentation method with substantial margins. Our code is available at https://***/Seonghoon-Yu/Zero-shot-RIS.

关键词： language reasoning vision

来源：评论

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Learning Rotation-Equivariant Features for Visual Correspondence

Learning Rotation-Equivariant Features for Visual Correspond...

引用

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

作者： Lee, Jongmin Kim, Byungjin Kim, Seungwook Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9798350301298

Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation.

关键词： Low-level vision

来源：评论

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CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending

CutMIB: Boosting Light Field Super-Resolution via Multi-View...

引用

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

作者： Xiao, Zeyu Liu, Yutong Gao, Ruisheng Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Data augmentation (DA) is an efficient strategy for improving the performance of deep neural networks. Recent DA strategies have demonstrated utility in single image super-resolution (SR). Little research has, however, focused on the DA strategy for light field SR, in which multi-view information utilization is required. For the first time in light field SR, we propose a potent DA strategy called CutMIB to improve the performance of existing light field SR networks while keeping their structures unchanged. Specifically, CutMIB first cuts low-resolution (LR) patches from each view at the same location. Then CutMIB blends all LR patches to generate the blended patch and finally pastes the blended patch to the corresponding regions of high-resolution light field views, and vice versa. By doing so, CutMIB enables light field SR networks to learn from implicit geometric information during the training stage. Experimental results demonstrate that CutMIB can improve the reconstruction performance and the angular consistency of existing light field SR networks. We further verify the effectiveness of CutMIB on real-world light field SR and light field denoising. The implementation code is available at https://***/zeyuxiao1997/CutMIB.

关键词： Low-level vision

来源：评论

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SFD2: Semantic-guided Feature Detection and Description

SFD2: Semantic-guided Feature Detection and Description

引用

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

作者： Xue, Fei Budvytis, Ignas Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9798350301298

Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant locally reliable features, resulting in limited efficiency and accuracy, especially in large-scale environments under challenging conditions. Instead, we propose to extract globally reliable features by implicitly embedding high-level semantics into both the detection and description processes. Specifically, our semantic-aware detector is able to detect keypoints from reliable regions (e.g. building, traffic lane) and suppress unreliable areas (e.g. sky, car) implicitly instead of relying on explicit semantic labels. This boosts the accuracy of keypoint matching by reducing the number of features sensitive to appearance changes and avoiding the need of additional segmentation networks at test time. Moreover, our descriptors are augmented with semantics and have stronger discriminative ability, providing more inliers at test time. Particularly, experiments on long-term large-scale visual localization Aachen Day-Night and RobotCar-Seasons datasets demonstrate that our model outperforms previous local features and gives competitive accuracy to advanced matchers but is about 2 and 3 times faster when using 2k and 4k keypoints, respectively. Code is available at https://***/feixue94/sfd2.

关键词： Low-level vision

来源：评论

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Photo-Realistic Image Restoration in the Wild with Controlled vision-Language Models

Photo-Realistic Image Restoration in the Wild with Controlle...

引用

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

作者： Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjolund, Jens Schon, Thomas B. Uppsala Univ Uppsala Sweden Karolinska Inst Stockholm Sweden

ISBN: (纸本)9798350365474

Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets. Typically, diffusion models trained in specific datasets fail to recover images that have out-of-distribution degradations. To address this problem, this work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild (wild IR). More specifically, all low-quality images are simulated with a synthetic degradation pipeline that contains multiple common degradations such as blur, resize, noise, and JPEG compression. Then we introduce robust training for a degradation-aware CLIP model to extract enriched image content features to assist high-quality image restoration. Our base diffusion model is the image restoration SDE (IR-SDE). Built upon it, we further present a posterior sampling strategy for fast noise-free image generation. We evaluate our model on both synthetic and real-world degradation datasets. Moreover, experiments on the unified image restoration task illustrate that the proposed posterior sampling improves image generation quality for various degradations.

关键词： Diffusion model Image restoration real-world Super-resolution

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Manipulating Transfer Learning for Property Inference

Manipulating Transfer Learning for Property Inference

引用

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

作者： Tian, Yulong Suya, Fnu Suri, Anshuman Xu, Fengyuan Evans, David Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Virginia Charlottesville VA USA

ISBN: (纸本)9798350301298

Transfer learning is a popular method for tuning pre-trained (upstream) models for different downstream tasks using limited data and computational resources. We study how an adversary with control over an upstream model used in transfer learning can conduct property inference attacks on a victim's tuned downstream model. For example, to infer the presence of images of a specific individual in the downstream training set. We demonstrate attacks in which an adversary can manipulate the upstream model to conduct highly effective and specific property inference attacks (AUC score > 0.9), without incurring significant performance loss on the main task. The main idea of the manipulation is to make the upstream model generate activations (intermediate features) with different distributions for samples with and without a target property, thus enabling the adversary to distinguish easily between downstream models trained with and without training examples that have the target property. Our code is available at https:// ***/yulongt23/Transfer-Inference.

关键词： accountability ethics in vision fairness privacy Transparency

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Re-thinking Federated Active Learning based on Inter-class Diversity

Re-thinking Federated Active Learning based on Inter-class D...

引用

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

作者： Kim, SangMook Bae, Sangmin Song, Hwanjun Yun, Se -Young KAIST AI Daejeon South Korea NAVER AI LAB Grenoble France

ISBN: (纸本)9798350301298

Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising solution. In the decentralized setting, there are two types of available query selector models, namely 'global' and 'local-only' models, but little literature discusses their performance dominance and its causes. In this work, we first demonstrate that the superiority of two selector models depends on the global and local interclass diversity. Furthermore, we observe that the global and local-only models are the keys to resolving the imbalance of each side. Based on our findings, we propose LoGo, a FAL sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. LoGo consistently outperforms six active learning strategies in the total number of 38 experimental settings. The code is available at: https://***/raymin0223/LoGo.

关键词： vision applications and systems

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Generalized UAV Object Detection via Frequency Domain Disentanglement

Generalized UAV Object Detection via Frequency Domain Disent...

引用

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

作者： Wang, Kunyu Fu, Xueyang Huang, Yukun Cao, Chengzhi Shi, Gege Zha, Zheng-Jun Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

When deploying the Unmanned Aerial Vehicles object detection (UAV-OD) network to complex and unseen real-world scenarios, the generalization ability is usually reduced due to the domain shift. To address this issue, this paper proposes a novel frequency domain disentanglement method to improve the UAV-OD generalization. Specifically, we first verified that the spectrum of different bands in the image has different effects to the UAV-OD generalization. Based on this conclusion, we design two learnable filters to extract domain-invariant spectrum and domain-specific spectrum, respectively. The former can be used to train the UAV-OD network and improve its capacity for generalization. In addition, we design a new instance-level contrastive loss to guide the network training. This loss enables the network to concentrate on extracting domain-invariant spectrum and domain-specific spectrum, so as to achieve better disentangling results. Experimental results on three unseen target domains demonstrate that our method has better generalization ability than both the baseline method and state-of-the-art methods.

关键词： detection recognition: Categorization retrieval

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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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