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Robust Federated Learning with Noisy and Heterogeneous Clients

Robust Federated Learning with Noisy and Heterogeneous Clien...

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

作者： Fang, Xiuwen Ye, Mang Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Inst Artificial IntelligenceHubei Key Lab Multim Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Model heterogeneous federated learning is a challenging task since each client independently designs its own model. Due to the annotation difficulty and free-riding participant issue, the local client usually contains unavoidable and varying noises, which cannot be effectively addressed by existing algorithms. This paper starts the first attempt to study a new and challenging robust federated learning problem with noisy and heterogeneous clients. We present a novel solution RHFL (Robust Heterogeneous Federated Learning), which simultaneously handles the label noise and performs federated learning in a single framework. It is featured in three aspects: (1) For the communication between heterogeneous models, we directly align the models feedback by utilizing public data, which does not require additional shared global models for collaboration. (2) For internal label noise, we apply a robust noise-tolerant loss function to reduce the negative effects. (3) For challenging noisy feedback from other participants, we design a novel client confidence re-weighting scheme, which adaptively assigns corresponding weights to each client in the collaborative learning stage. Extensive experiments validate the effectiveness of our approach in reducing the negative effects of different noise rates/types under both model homogeneous and heterogeneous federated learning settings, consistently outperforming existing methods.

关键词： Adaptation models Privacy computer vision Computational modeling Collaboration Collaborative work Data models

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Learning Non-target Knowledge for Few-shot Semantic Segmentation

Learning Non-target Knowledge for Few-shot Semantic Segmenta...

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

作者： Liu, Yuanwei Liu, Nian Cao, Qinglong Yao, Xiwen Han, Junwei Shao, Ling Northwestern Polytech Univ Xian Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Terminus Grp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in nontarget regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL-5(i) and COCO-20(i) datasets show that our approach is effective despite its simplicity. Code is available at https://***/LIUYANWEI98/NERTNet

关键词： computer vision Codes Shape Semantics Prototypes Object segmentation Filtering algorithms

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Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers 32

Modulating Image Restoration with Continual Levels via Adapt...

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

作者： He, Jingwen Dong, Chao Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

In image restoration tasks, like denoising and super-resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of little additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.

关键词： Deep Learning Low-level vision

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Causal Hidden Markov Model for Time Series Disease Forecasting

Causal Hidden Markov Model for Time Series Disease Forecasti...

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

作者： Li, Jing Wu, Botong Sun, Xinwei Wang, Yizhou Peking Univ Dept Comp Sci Beijing Peoples R China Peking Univ Adv Inst Info Tech Beijing Peoples R China Peking Univ Ctr Frontiers Comp Studies Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part;b) the disease (non-clinical)-related part;c) others, with only a),b) causally related to the disease however c) may contain spurious correlations (with the disease) inherited from the data provided. With personal attributes and disease label respectively provided as side information and supervision, we prove that these disease-related hidden variables can be disentangled from others, implying the avoidance of spurious correlation for generalization to medical data from other (out-of-) distributions. Guaranteed by this result, we propose a sequential variational auto-encoder with a reformulated objective function. We apply our model to the early prediction of peripapillary atrophy and achieve promising results on out-of-distribution test data. Further, the ablation study empirically shows the effectiveness of each component in our method. And the visualization shows the accurate identification of lesion regions from others. (1)

关键词： Correlation Time series analysis Hidden Markov models Medical treatment Predictive models Linear programming pattern recognition

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Multi-shot Temporal Event Localization: a Benchmark

Multi-shot Temporal Event Localization: a Benchmark

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

作者： Liu, Xiaolong Hu, Yao Bai, Song Ding, Fei Bai, Xiang Torr, Philip H. S. Huazhong Univ Sci & Technol Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Current developments in temporal event or action localization usually target actions captured by a single camera. However, extensive events or actions in the wild may be captured as a sequence of shots by multiple cameras at different positions. In this paper, we propose a new and challenging task called multi-shot temporal event localization, and accordingly, collect a large-scale dataset called MUlti-Shot EventS (MUSES). MUSES has 31,477 event instances for a total of 716 video hours. The core nature of MUSES is the frequent shot cuts, for an average of 19 shots per instance and 176 shots per video, which induces large intra-instance variations. Our comprehensive evaluations show that the state-of-the-art method in temporal action localization only achieves an mAP of 13.1% at IoU=0.5. As a minor contribution, we present a simple baseline approach for handling the intra-instance variations, which reports an mAP of 18.9% on MUSES and 56.9% on THUMOS14 at IoU=0.5. To facilitate research in this direction, we release the dataset and the project code at https://***/muses/.

关键词： Location awareness computer vision TV Codes Annotations Benchmark testing Motion pictures

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Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds

Voxel Set Transformer: A Set-to-Set Approach to 3D Object De...

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

作者： He, Chenhang Li, Ruihuang Li, Shuai Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to compute the self-attention on large-scale point cloud data because point cloud is a long sequence and unevenly distributed in 3D space. To solve this issue, existing methods usually compute self-attention locally by grouping the points into clusters of the same size, or perform convolutional self-attention on a discretized representation. However, the former results in stochastic point dropout, while the latter typically has narrow attention fields. In this paper, we propose a novel voxel-based architecture, namely Voxel Set Transformer (VoxSeT), to detect 3D objects from point clouds by means of set-to-set translation. VoxSeT is built upon a voxel-based set attention (VSA) module, which reduces the self-attention in each voxel by two cross-attentions and models features in a hidden space induced by a group of latent codes. With the VSA module, VoxSeT can manage voxelized point clusters with arbitrary size in a wide range, and process them in parallel with linear complexity. The proposed VoxSeT integrates the high performance of transformer with the efficiency of voxel-based model, which can be used as a good alternative to the convolutional and point-based backbones. VoxSeT reports competitive results on the KITH and Waymo detection benchmarks. The source codes can be found at https://***/skyhehe123/VoxSet.

关键词： Point cloud compression Convolutional codes computer vision Three-dimensional displays Stochastic processes Distributed databases Object detection

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Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes 32

Look More Than Once: An Accurate Detector for Text of Arbitr...

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

作者： Zhang, Chengquan Liang, Borong Huang, Zuming En, Mengyi Han, Junyu Ding, Errui Ding, Xinghao Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Xiamen Univ Fujian Key Lab Sensing & Comp Smart City Xiamen Peoples R China

ISBN: (纸本)9781728132938

Previous scene text detection methods have progressed substantially over the past years. However, limited by the receptive field of CNNs and the simple representations like rectangle bounding box or quadrangle adopted to describe text, previous methods may fall short when dealing with more challenging text instances, such as extremely long text and arbitrarily shaped text. To address these two problems, we present a novel text detector namely LOMO, which localizes the text progressively for multiple times (or in other word, LOok More than Once). LOMO consists of a direct regressor (DR), an iterative refinement module (IRM) and a shape expression module (SEM). At first, text proposals in the form of quadrangle are generated by DR branch. Next, IRM progressively perceives the entire long text by iterative refinement based on the extracted feature blocks of preliminary proposals. Finally, a SEM is introduced to reconstruct more precise representation of irregular text by considering the geometry properties of text instance, including text region, text center line and border offsets. The state-of-the-art results on several public benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015 and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.

关键词： Categorization Document Analysis Grouping and Shape recognition: Detection Retrieval Segmentation vision Applicat

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Not just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction

Not just Compete, but Collaborate: Local Image-to-Image Tran...

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

作者： Kim, Daejin Khan, Mohammad Azam Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea Dhaka Power Distribut Co Ltd Dhaka Bangladesh

ISBN: (纸本)9781665445092

Facial attribute editing aims to manipulate the image with the desired attribute while preserving the other details. Recently, generative adversarial networks along with the encoder-decoder architecture have been utilized for this task owing to their ability to create realistic images. However, the existing methods for the unpaired dataset cannot still preserve the attribute-irrelevant regions properly due to the absence of the ground truth image. This work proposes a novel, intuitive loss function called the CAM-consistency loss, which improves the consistency of an input image in image translation. While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator. Our CAM-consistency loss directly optimizes such a Grad-CAM output from the discriminator during training, in order to properly capture which local regions the generator should change while keeping the other regions unchanged. In this manner, our approach allows the generator and the discriminator to collaborate with each other to improve the image translation quality. In our experiments, we validate the effectiveness and versatility of our proposed CAM-consistency loss by applying it to several representative models for facial image editing, such as StarGAN, AttGAN, and STGAN.

关键词： Training computer vision Computational modeling computer architecture Predictive models Generative adversarial networks Generators

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Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction

Direct Voxel Grid Optimization: Super-fast Convergence for R...

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

作者： Sun, Cheng Sun, Min Chen, Hwann-Tzong Natl Tsing Hua Univ Hsinchu Taiwan ASUS AICS Dept Singapore Singapore Joint Res Ctr AI Technol & All Vista Healthcare Taipei Taiwan Aeolus Robot Taipei Taiwan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses. This task, which is often applied to novel view synthesis, is recently revolutionized by Neural Radiance Field (NeRF) for its state-of-the-art quality and flexibility. However, NeRF and its variants require a lengthy training time ranging from hours to days for a single scene. In contrast, our approach achieves NeRF-comparable quality and converges rapidly from scratch in less than 15 minutes with a single GPU. We adopt a representation consisting of a density voxel grid for scene geometry and a feature voxel grid with a shallow network for complex view-dependent appearance. Modeling with explicit and discretized volume representations is not new, but we propose two simple yet non-trivial techniques that contribute to fast convergence speed and high-quality output. First, we introduce the post-activation interpolation on voxel density, which is capable of producing sharp surfaces in lower grid resolution. Second, direct voxel density optimization is prone to suboptimal geometry solutions, so we robustify the optimization process by imposing several priors. Finally, evaluation on five inward-facing benchmarks shows that our method matches, if not surpasses, NeRF's quality, yet it only takes about 15 minutes to train from scratch for a new scene. Code: https:// ***/sunset1995/DirectVoxGO.

关键词： Training Geometry Solid modeling Interpolation pattern recognition Task analysis Optimization

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ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation

ABMDRNet: Adaptive-weighted Bi-directional Modality Differen...

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

作者： Zhang, Qiang Zhao, Shenlu Luo, Yongjiang Zhang, Dingwen Huang, Nianchang Han, Jungong Xidian Univ Sch Mechano Elect Engn Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth Dyfed Wales

ISBN: (纸本)9781665445092

Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weighted summation, to fuse features from different modalities. These strategies, unfortunately, overlook the modality differences due to different imaging mechanisms, so that they suffer from the reduced discriminability of the fused features. To address such an issue, we propose, for the first time, the strategy of bridging-then-fusing, where the innovation lies in a novel Adaptive-weighted Bi-directional Modality Difference Reduction Network (ABMDRNet). Concretely, a Modality Difference Reduction and Fusion (MDRF) subnetwork is designed, which first employs a bi-directional image-to-image translation based method to reduce the modality differences between RGB features and thermal features, and then adaptively selects those discriminative multi-modality features for RGB-T semantic segmentation in a channel-wise weighted fusion way. Furthermore, considering the importance of contextual information in semantic segmentation, a Multi-Scale Spatial Context (MSC) module and a Multi-Scale Channel Context (MCC) module are proposed to exploit the interactions among multi-scale contextual information of cross-modality features together with their long-range dependencies along spatial and channel dimensions, respectively. Comprehensive experiments on MFNet dataset demonstrate that our method achieves new state-of-the-art results.

关键词： Image segmentation Technological innovation computer vision Fuses Semantics Lighting Imaging

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