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Rethinking Federated Learning with Domain Shift: A Prototype View

Rethinking Federated Learning with Domain Shift: A Prototype...

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conference on computer vision and pattern recognition (CVPR)

作者： Wenke Huang Mang Ye Zekun Shi He Li Bo Du National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

Federated learning shows a bright promise as a privacy-preserving collaborative learning technique. However, prevalent solutions mainly focus on all private data sampled from the same domain. An important challenge is that when distributed data are derived from diverse domains. The private model presents degenerative performance on other domains (with domain shift). Therefore, we expect that the global model optimized after the federated learning process stably provides generalizability performance on multiple domains. In this paper, we propose Federated Proto-types Learning (FPL) for federated learning under domain shift. The core idea is to construct cluster prototypes and unbiased prototypes, providing fruitful domain knowledge and a fair convergent target. On the one hand, we pull the sample embedding closer to cluster prototypes belonging to the same semantics than cluster prototypes from distinct classes. On the other hand, we introduce consistency regularization to align the local instance with the respective unbiased prototype. Empirical results on Digits and Office Caltech tasks demonstrate the effectiveness of the proposed solution and the efficiency of crucial modules.

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A Practical Upper Bound for the Worst-Case Attribution Deviations

A Practical Upper Bound for the Worst-Case Attribution Devia...

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conference on computer vision and pattern recognition (CVPR)

作者： Fan Wang Adams Wai-Kin Kong School of Computer Science and Engineering Nanyang Technological University Rapid-Rich Object Search (ROSE) Lab IGP Nanyang Technological University

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks; however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both $\ell_{2}$ and $\ell_{\infty}$ -norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities.

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Compacting Binary Neural Networks by Sparse Kernel Selection

Compacting Binary Neural Networks by Sparse Kernel Selection

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yikai Wang Wenbing Huang Yinpeng Dong Fuchun Sun Anbang Yao Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems BNRist Center Tsinghua University Gaoling School of Artificial Intelligence Renmin University of China RealAI Intel Labs China

Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning non-repetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full code-book. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets.

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ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts

ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhida Feng Zhenyu Zhang Xintong Yu Yewei Fang Lanxin Li Xuyi Chen Yuxiang Lu Jiaxiang Liu Weichong Yin Shikun Feng Yu Sun Li Chen Hao Tian Hua Wu Haifeng Wang Baidu Inc. School of Computer Science and Technology Wuhan University of Science and Technology

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 1 not only achieves a new state-of-the-art on MS-COCO with zero-shot FID-30k score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300.

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Efficient RGB-T Tracking via Cross-Modality Distillation

Efficient RGB-T Tracking via Cross-Modality Distillation

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conference on computer vision and pattern recognition (CVPR)

作者： Tianlu Zhang Hongyuan Guo Qiang Jiao Qiang Zhang Jungong Han School of Mechano-Electronic Engineering Xidian University China Department of Computer Science the University of Sheffield UK Centre for Machine Intelligence the University of Sheffield UK

Most current RGB-T trackers adopt a two-stream structure to extract unimodal RGB and thermal features and complex fusion strategies to achieve multi-modal feature fusion, which require a huge number of parameters, thus hindering their real-life applications. On the other hand, a compact RGB-T tracker may be computationally efficient but encounter non-negligible performance degradation, due to the weakening of feature representation ability. To remedy this situation, a cross-modality distillation framework is presented to bridge the performance gap between a compact tracker and a powerful tracker. Specifically, a specific-common feature distillation module is proposed to transform the modality-common information as well as the modality-specific information from a deeper two-stream network to a shallower single-stream network. In addition, a multi-path selection distillation module is proposed to instruct a simple fusion module to learn more accurate multi-modal information from a well-designed fusion mechanism by using multiple paths. We validate the effectiveness of our method with extensive experiments on three RGB-T benchmarks, which achieves state-of-the-art performance but consumes much less computational resources.

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Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks

Minimizing Maximum Model Discrepancy for Transferable Black-...

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conference on computer vision and pattern recognition (CVPR)

作者： Anqi Zhao Tong Chu Yahao Liu Wen Li Jingjing Li Lixin Duan School of Computer Science and Engineering University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China

In this work, we study the black-box targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for black-box targeted attacks, which gives a rigorous theoretical analysis for guaranteeing the success of the attack. We reveal that the attack error on a target model mainly depends on empirical attack error on the substitute model and the maximum model discrepancy among substitute models. On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy (M3D) of the substitute models when training the generator to generate adversarial examples. In this way, our model is capable of crafting highly transferable adversarial examples that are robust to the model variation, thus improving the success rate for attacking the black-box model. We conduct extensive experiments on the ImageNet dataset with different classification models, and our proposed approach outperforms existing state-of-the-art methods by a significant margin. The code will be available at https://***/Asteriajojo/M3D.

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DIP: Dual Incongruity Perceiving Network for Sarcasm Detection

DIP: Dual Incongruity Perceiving Network for Sarcasm Detecti...

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conference on computer vision and pattern recognition (CVPR)

作者： Changsong Wen Guoli Jia Jufeng Yang TMCC College of Computer Science Nankai University China

Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the popularity and complementarity of image-text data, we investigate the task of multi-modal sarcasm detection. Different from other multi-modal tasks, for the sarcastic data, there exists intrinsic incongruity between a pair of image and text as demonstrated in psychological theories. To tackle this issue, we propose a Dual Incongruity Perceiving (DIP) network consisting of two branches to mine the sarcastic information from factual and affective levels. For the factual aspect, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings, and leverage gaussian distribution to model the uncertain correlation caused by the incongruity. The distribution is generated from the latest data stored in the memory bank, which can adaptively model the difference of semantic similarity between sarcastic and non-sarcastic data. For the affective aspect, we utilize siamese layers with shared parameters to learn cross-modal sentiment information. Furthermore, we use the polarity value to construct a relation graph for the mini-batch, which forms the continuous contrastive loss to acquire affective embeddings. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art approaches. Our code is released on https://***/downdric/MSD.

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Texture-Guided Saliency Distilling for Unsupervised Salient Object Detection

Texture-Guided Saliency Distilling for Unsupervised Salient ...

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conference on computer vision and pattern recognition (CVPR)

作者： Huajun Zhou Bo Qiao Lingxiao Yang Jianhuang Lai Xiaohua Xie School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China

Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundaries. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance. Code is available at ***/moothes/A2S-v2.

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Four-view Geometry with Unknown Radial Distortion

Four-view Geometry with Unknown Radial Distortion

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conference on computer vision and pattern recognition (CVPR)

作者： Petr Hruby Viktor Korotynskiy Timothy Duff Luke Oeding Marc Pollefeys Tomas Pajdla Viktor Larsson Dept. of Computer Science ETH Zürich CIIRC CTU in Prague University of Washington Auburn University Lund University

We present novel solutions to previously unsolved prob-lems of relative pose estimation from images whose calibration parameters, namely focal lengths and radial distortion, are unknown. Our approach enables metric reconstruction without modeling these parameters. The minimal case for reconstruction requires 13 points in 4 views for both the calibrated and uncalibrated cameras. We describe and implement the first solution to these minimal problems. In the calibrated case, this may be modeled as a polynomial sys-tem of equations with 3584 solutions. Despite the apparent intractability, the problem decomposes spectacularly. Each solution falls into a Euclidean symmetry class of size 16, and we can estimate 224 class representatives by solving a sequence of three subproblems with 28, 2, and 4 solutions. We highlight the relationship between internal constraints on the radial quadrifocal tensor and the relations among the principal minors of a $4\times 4$ matrix. We also address the case of 4 upright cameras, where 7 points are minimal. Finally, we evaluate our approach on simulated and real data and benchmark against previous calibration-free solutions, and show that our method provides an efficient startup for an SfM pipeline with radial cameras.

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Randomized Adversarial Training via Taylor Expansion

Randomized Adversarial Training via Taylor Expansion

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conference on computer vision and pattern recognition (CVPR)

作者： Gaojie Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang State Key Laboratory of Computer Science Institute of Software CAS Beijing China University of Liverpool Liverpool UK Lancaster University Lancaster UK

In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the most successful methods. To deal with both the robustness against adversarial examples and the accuracy over clean examples, many works develop enhanced adversarial training methods to achieve various trade-offs between them [[19], [38], [80]], Leveraging over the studies [8], [32] that smoothed update on weights during training may help find flat minima and improve generalization, we suggest reconciling the robustness-accuracy trade-off from another perspective, i.e., by adding random noise into deterministic weights. The randomized weights enable our design of a novel adversarial training method via Taylor expansion of a small Gaussian noise, and we show that the new adversarial training method can flatten loss landscape and find flat minima. With PGD, CW, and Auto Attacks, an extensive set of experiments demonstrate that our method enhances the state-of-the-art adversarial training methods, boosting both robustness and clean accuracy. The code is available at https://***/Alexkael/Randomized-Adversarial-Training.

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