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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiawei Zhang Jiahe Li Lei Huang Xiaohan Yu Lin Gu Jin Zheng Xiao Bai State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Jiangxi Research Institute Beihang University SKLCCSE Institute of Artificial Intelligence Beihang University School of Computing Macquarie University Australia RIKEN AIP The University of Tokyo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However, few studies have investigated the robustness after fine-tuning them in real-world scenarios, during which the domain generalization ability can be seriously degraded. In this paper, we explore fine-tuning stereo matching networks without compromising their robustness to unseen domains. Our motivation stems from comparing Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning: GT degrades, but PL preserves the domain generalization ability. Empirically, we find the difference between GT and PL implies valuable information that can regularize networks during fine-tuning. We also propose a framework to utilize this difference for fine-tuning, consisting of a frozen Teacher, an exponential moving average (EMA) Teacher, and a Student network. The core idea is to utilize the EMA Teacher to measure what the Student has learned and dynamically improve GT and PL for fine-tuning. We integrate our framework with state-of-the-art networks and evaluate its effectiveness on several real-world datasets. Extensive experiments show that our method effectively preserves the domain generalization ability during fine-tuning. Code is available at: https://***/jiaw-z/DKT-Stereo.

关键词： computer vision Codes Robustness Data models Pattern matching Synthetic data Meteorology

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DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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SAMRS: scaling-up remote sensing segmentation dataset with segment anything model 23

SAMRS: scaling-up remote sensing segmentation dataset with s...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Di Wang Jing Zhang Bo Du Minqiang Xu Lin Liu Dacheng Tao Liangpei Zhang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at https://***/ViTAE-Transformer/SAMRS.

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SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

arXiv

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arXiv 2023年

作者： Wang, Di Zhang, Jing Du, Bo Xu, Minqiang Liu, Lin Tao, Dacheng Zhang, Liangpei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

关键词： Remote sensing

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DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

arXiv

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arXiv 2023年

作者： Guo, Haonan Du, Bo Wu, Chen Han, Chengxi Zhang, Liangpei The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China The National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently propelled automatic CD into a technological revolution. Nevertheless, deep learning-based CD methods are still plagued by two primary issues: 1) insufficient temporal relationship modeling and 2) pseudo-change misclassification. To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD. Firstly, we designed a hard sample-aware contrastive loss, which reweights the importance of hard and simple samples. This loss allows for explicit modeling of the temporal correlation between bi-temporal remote sensing images. Furthermore, the modeled temporal relations are utilized as knowledge prior to guide the segmentation process for detecting change regions. The DeepCL framework is thoroughly evaluated both theoretically and experimentally, demonstrating its superior feature discriminability, resilience against pseudo changes, and adaptability to a variety of CD algorithms. Extensive comparative experiments substantiate the quantitative and qualitative superiority of DeepCL over state-of-the-art CD approaches. The source code will be made available at https://***/HaonanGuo/DeepCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Change detection

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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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Freevc: Towards High-Quality Text-Free One-Shot Voice Conversion

Freevc: Towards High-Quality Text-Free One-Shot Voice Conver...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingyi Li Weiping Tu Li Xiao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Hubei Luojia Laboratory China

Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information. However, current approaches normally either extract dirty content information with speaker information leaked in, or demand a large amount of annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for high-quality waveform reconstruction, and propose strategies for clean content information extraction without text annotation. We disentangle content information by imposing an information bottleneck to WavLM features, and propose the spectrogram-resize based data augmentation to improve the purity of extracted content information. Experimental results show that the proposed method outperforms the latest VC models trained with annotated data and has greater robustness.

关键词： Training Annotations Vocoders Signal processing Feature extraction Information retrieval Data models

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Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning

Unsupervised Visible-Infrared Person Re-Identification via P...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zesen Wu Mang Ye Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

Unsupervised visible-infrared person re-identification is a challenging task due to the large modality gap and the unavailability of cross-modality correspondences. Cross-modality correspondences are very crucial to bridge the modality gap. Some existing works try to mine cross-modality correspondences, but they focus only on local information. They do not fully exploit the global relationship across identities, thus limiting the quality of the mined correspondences. Worse still, the number of clusters of the two modalities is often inconsistent, exacerbating the unreliability of the generated correspondences. In response, we devise a Progressive Graph Matching method to globally mine cross-modality correspondences under cluster imbalance scenarios. PGM formulates correspondence mining as a graph matching process and considers the global information by minimizing the global matching cost, where the matching cost measures the dissimilarity of clusters. Besides, PGM adopts a progressive strategy to address the imbalance issue with multiple dynamic matching processes. Based on PGM, we design an Alternate Cross Contrastive Learning (ACCL) module to reduce the modality gap with the mined cross-modality correspondences, while mitigating the effect of noise in correspondences through an alternate scheme. Extensive experiments demonstrate the reliability of the generated correspondences and the effectiveness of our method.

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Towards Modality-Agnostic Person Re-identification with Descriptive Query

Towards Modality-Agnostic Person Re-identification with Desc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Cuiqun Chen Mang Ye Ding Jiang National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

Person re-identification (ReID) with descriptive query (text or sketch) provides an important supplement for general image-image paradigms, which is usually studied in a single cross-modality matching manner, e.g., text-to-image or sketch-to-photo. However, without a camera-captured photo query, it is uncertain whether the text or sketch is available or not in practical scenarios. This motivates us to study a new and challenging modality-agnostic person re-ideruification problem. Towards this goal, we propose a unified person re-identification (UNIReID) architecture that can effectively adapt to cross-modality and multi-modality tasks. Specifically, UNIReID incorporates a simple dual-encoder with task-specific modality learning to mine and fuse visual and textual modality information. To deal with the imbalanced training problem of different tasks in UNIReID, we propose a task-aware dynamic training strategy in terms of task difficulty, adaptively adjusting the training focus. Besides, we construct three multi-modal ReID datasets by collecting the corresponding sketches from photos to support this challenging study. The experimental results on three multi-modal ReID datasets show that our UNIReID greatly improves the retrieval accuracy and generalization ability on different tasks and unseen scenarios.

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Contextual Graph Reconstruction and Emotional Variation Learning for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Rao, Yujing Cao, Min Ye, Mang Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan430072 China Soochow University School of Computer Science and Technology Suzhou215006 China

Conversational Emotion Recognition (CER) significantly benefits from the integration of multiple modalities. However, real-world scenarios are often plagued by hardware malfunctions and network failures that lead to missing modalities and incomplete emotional representations. Existing methods primarily focus on modeling inter-modal relationships within isolated utterances to generate missing data, so they do not adequately capture conversational context and dynamic emotional evolution. In conversations, emotional expressions are inherently context-dependent and dynamic. Insufficient modeling of these properties can substantially degrade performance. To address these challenges, we propose Contextual Graph Reconstruction and Emotional Variation Learning (CGR-EVL). Our approach flexibly constructs an utterance graph with diverse coverage based on speaker activity and modality availability, thereby capturing more contextual information. To enhance the handling of missing modalities, we further integrate temporal relations through a relational graph convolutional network to reconstruct missing features and introduce a speaker emotion-aware constraint to ensure emotional coherence. Additionally, we propose the concept of emotional entropy to quantify variation patterns and develop a novel loss function that aligns predicted and actual variations. Experiments on the IEMOCAP and MELD datasets show that CGR-EVL outperforms state-of-the-art methods, particularly under conditions of incomplete modalities. © 2010-2012 IEEE.

关键词： Graph neural networks

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