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Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第6期44卷 2841-2855页

作者： Gong, Chen Yang, Jian You, Jane Sugiyama, Masashi Nanjing Univ Sci & Technol Intelligent Percept Key Lab Intelligent Percept & Syst High Dimens In PCA LabSch Comp Sci & Engn Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn PCA Lab Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210044 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Univ Tokyo Grad Sch Frontier Sci Chiba 2778574 Japan

In this paper, we propose a general framework termed centroid estimation with guaranteed efficiency (CEGE) for weakly supervised learning (WSL) with incomplete, inexact, and inaccurate supervision. The core of our framework is to devise an unbiased and statistically efficient risk estimator that is applicable to various weak supervision. Specifically, by decomposing the loss function (e.g., the squared loss and hinge loss) into a label-independent term and a label-dependent term, we discover that only the latter is influenced by the weak supervision and is related to the centroid of the entire dataset. Therefore, by constructing two auxiliary pseudo-labeled datasets with synthesized labels, we derive unbiased estimates of centroid based on the two auxiliary datasets, respectively. These two estimates are further linearly combined with a properly decided coefficient which makes the final combined estimate not only unbiased but also statistically efficient. This is better than some existing methods that only care about the unbiasedness of estimation but ignore the statistical efficiency. The good statistical efficiency of the derived estimator is guaranteed as we theoretically prove that it acquires the minimum variance when estimating the centroid. As a result, intensive experimental results on a large number of benchmark datasets demonstrate that our CEGE generally obtains better performance than the existing approaches related to typical WSL problems including semi-supervised learning, positive-unlabeled learning, multiple instance learning, and label noise learning.

关键词： Estimation Supervised learning Fasteners Training data Support vector machines Semisupervised learning Safety Weakly supervised learning centroid estimation unbiasedness statistical efficiency

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Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking 39

Exploiting Multimodal Spatial-temporal Patterns for Video Ob...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hu, Xiantao Tai, Ying Zhao, Xu Zhao, Chen Zhang, Zhenyu Li, Jun Zhong, Bineng Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Nanjing University China Guangxi Normal University China

ISBN: (纸本)157735897X

Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusion and enhancement of spatial features or merely leverage the sparse temporal relationships between video frames. These approaches do not fully exploit the temporal correlations in multimodal videos, making it difficult to capture the dynamic changes and motion information of targets in complex scenarios. To alleviate this problem, we propose a unified multimodal spatial-temporal tracking approach named STTrack. In contrast to previous paradigms that solely relied on updating reference information, we introduced a temporal state generator (TSG) that continuously generates a sequence of tokens containing multimodal temporal information. These temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the target. Furthermore, at the spatial level, we introduced the mamba fusion and background suppression interactive (BSI) modules. These modules establish a dual-stage mechanism for coordinating information interaction and fusion between modalities. Extensive comparisons on five benchmark datasets illustrate that STTrack achieves state-of-the-art performance across various multimodal tracking scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Object detection

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Unsupervised Person Re-Identification Based on Intermediate Domains 14

Unsupervised Person Re-Identification Based on Intermediate ...

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14th International Conference on Graphics and Image Processing (ICGIP)

作者： Jiao, Haijie Ding, Mengyuan Zhang, Shanshan Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In Jiangsu Key Lab Image & Video Understanding Socia Sch Comp Sci & EngnMinist EducPCA Lab Nanjing Peoples R China

ISBN: (纸本)9781510666313;9781510666320

Unsupervised domain adaptive person re-identification (UDA re-ID) aims to transfer knowledge learned from the labeled source domain to the unlabeled target domain. Most recent methods focus on narrowing down the domain gap between the source and target domains while ignore the bridge between them. In this work, we explicitly model appropriate intermediate domains and construct two adaptation pairs ("source-intermediate" and "intermediate-target") instead of the original "source-target" one pair adaptation. The purpose is to ease the adaptation difficulties caused by large domain gaps, making the adaptation process more smooth. To generate the intermediate domain, we use image-to-image translation methods which generate images that have the same contents and ID labels shared with the source domain and similar style to the target domain. When evaluated on standard benchmarks, our proposed methods outperforms the state of the arts by a large margin on the target domains where mAP of our method is higher than IDM [12] by 1.4% and 2.2% when testing on Market-1501 and MSMT17 respectively.

关键词： Domain adaptation Person re-id Intermediate domain

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Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval 36

Domain Disentangled Generative Adversarial Network for Zero-...

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36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence

作者： Xu, Rui Han, Zongyan Hui, Le Qian, Jianjun Xie, Jin Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Key Lab Intelligent Percept & Syst High Dimens In PCA LabMinist EducSch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781577358763

Sketch-based 3D shape retrieval is a challenging task due to the large domain discrepancy between sketches and 3D shapes. Since existing methods are trained and evaluated on the same categories, they cannot effectively recognize the categories that have not been used during training. In this paper, we propose a novel domain disentangled generative adversarial network (DD-GAN) for zero-shot sketch-based 3D retrieval, which can retrieve the unseen categories that are not accessed during training. Specifically, we first generate domain-invariant features and domain-specific features by disentangling the learned features of sketches and 3D shapes, where the domain-invariant features are used to align with the corresponding word embeddings. Then, we develop a generative adversarial network that combines the domain-specific features of the seen categories with the aligned domain-invariant features to synthesize samples, where the synthesized samples of the unseen categories are generated by using the corresponding word embeddings. Finally, we use the synthesized samples of the unseen categories combined with the real samples of the seen categories to train the network for retrieval, so that the unseen categories can be recognized. In order to reduce the domain shift problem, we utilize unlabeled unseen samples to enhance the discrimination ability of the discriminator. With the discriminator distinguishing the generated samples from the unlabeled unseen samples, the generator can generate more realistic unseen samples. Extensive experiments on the SHREC' 13 and SHREC' 14 datasets show that our method significantly improves the retrieval performance of the unseen categories.

关键词： Generative adversarial networks

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Learning Graph Convolutional Networks for Multi-label Recognition and Applications

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第6期45卷 6969-6983页

作者： Chen, Zhao-Min Wei, Xiu-Shen Wang, Peng Guo, Yanwen Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China Megvii Technol Megvii Res Nanjing Nanjing 210000 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn PCA LabMinist Educ Key Lab Intelligent Percept & Syst High Dimens Inf Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China Univ Wollongong Sydney NSW 2170 Australia

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important information, we propose graph convolutional networks (GCNs) based models for multi-label image recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier Learning GCN (C-GCN) to map class-level semantic representations (e.g., word embeddings) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-aware features and propose prediction learning GCN (P-GCN) to encode such features into inter-dependent image-level prediction scores. Furthermore, we also present an effective correlation matrix construction approach to capture inter-class relationships and consequently guide information propagation among classes. Empirical results on generic multi-label image recognition demonstrate that both of the proposed models can obviously outperform other existing state-of-the-arts. Moreover, the proposed methods also show advantages in some other multi-label classification related applications.

关键词： Image recognition Correlation Face recognition Task analysis Semantics Topology Computational modeling Multi-label recognition graph convolutional networks convolutional neural networks label dependency

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Few-shot Continual Infomax Learning

Few-shot Continual Infomax Learning

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International Conference on Computer Vision (ICCV)

作者： Ziqi Gu Chunyan Xu Jian Yang Zhen Cui PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Few-shot continual learning is the ability to continually train a neural network from a sequential stream of few-shot data. In this paper, we propose a Few-shot Continual Infomax Learning (FCIL) framework that makes a deep model to continually/incrementally learn new concepts from few labeled samples, relieving the catastrophic forgetting of past knowledge. Specifically, inspired by the theoretical definition of transfer entropy, we introduce a feature embedding infomax to effectively perform the few-shot learning, which can transfer the strong encoding capability of the base network to learn the feature embedding of these novel classes by maximizing the mutual information of different-level feature distributions. Further, considering that the learned knowledge in the human brain is a generalization of actual information and exists in a certain relational structure, we perform continual structure infomax learning to relieve the catastrophic forgetting problem in the continual learning process. The information structure of this learned knowledge can be preserved through maximizing the mutual information across these continual-changing relations of inter-classes. Comprehensive evaluations on CIFAR100, miniImageNet, and CUB200 datasets demonstrate the superiority of our FCIL when compared against state-of-the-art methods on the few-shot continual learning task.

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Incomplete multimodality-diffused emotion recognition 23

Incomplete multimodality-diffused emotion recognition

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

作者： Yuanzhi Wang Yong Li Zhen Cui PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Human multimodal emotion recognition (MER) aims to perceive and understand human emotions via various heterogeneous modalities, such as language, vision, and acoustic. Compared with unimodality, the complementary information in the multimodalities facilitates robust emotion understanding. Nevertheless, in real-world scenarios, the missing modalities hinder multimodal understanding and result in degraded MER performance. In this paper, we propose an Incomplete Multimodality-Diffused emotion recognition (IMDer) method to mitigate the challenge of MER under incomplete multimodalities. To recover the missing modalities, IMDer exploits the score-based diffusion model that maps the input Gaussian noise into the desired distribution space of the missing modalities and recovers missing data abided by their original distributions. Specially, to reduce semantic ambiguity between the missing and the recovered modalities, the available modalities are embedded as the condition to guide and refine the diffusion-based recovering process. In contrast to previous work, the diffusion-based modality recovery mechanism in IMDer allows to simultaneously reach both distribution consistency and semantic disambiguation. Feature visualization of the recovered modalities illustrates the consistent modality-specific distribution and semantic alignment. Besides, quantitative experimental results verify that IMDer obtains state-of-the-art MER accuracy under various missing modality patterns. Codes are released at https://***/mdswyz/IMDer.

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MILP-StuDio: MILP Instance Generation via Block Structure Decomposition 38

MILP-StuDio: MILP Instance Generation via Block Structure De...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Haoyang Wang, Jie Zhang, Wanbo Geng, Zijie Kuang, Yufei Li, Xijun Zhang, Yongdong Li, Bin Wu, Feng MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Shanghai Jiao Tong University China Noah's Ark Lab Huawei Technologies Canada

Mixed-integer linear programming (MILP) is one of the most popular mathematical formulations with numerous applications. In practice, improving the performance of MILP solvers often requires a large amount of high-quality data, which can be challenging to collect. Researchers thus turn to generation techniques to generate additional MILP instances. However, existing approaches do not take into account specific block structures-which are closely related to the problem formulations-in the constraint coefficient matrices (CCMs) of MILPs. Consequently, they are prone to generate computationally trivial or infeasible instances due to the disruptions of block structures and thus problem formulations. To address this challenge, we propose a novel MILP generation framework, called Block Structure Decomposition (MILP-StuDio), to generate high-quality instances by preserving the block structures. Specifically, MILP-StuDio begins by identifying the blocks in CCMs and decomposing the instances into block units, which serve as the building blocks of MILP instances. We then design three operators to construct new instances by removing, substituting, and appending block units in the original instances, enabling us to generate instances with flexible sizes. An appealing feature of MILP-StuDio is its strong ability to preserve the feasibility and computational hardness of the generated instances. Experiments on commonly-used benchmarks demonstrate that with instances generated by MILP-StuDio, the learning-based solvers are able to significantly reduce over 10% of the solving time. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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Distribution-Consistent Modal Recovering for Incomplete Multimodal Learning

Distribution-Consistent Modal Recovering for Incomplete Mult...

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International Conference on Computer Vision (ICCV)

作者： Yuanzhi Wang Zhen Cui Yong Li PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recovering missing modality is popular in incomplete multimodal learning because it usually benefits downstream tasks. However, the existing methods often directly estimate missing modalities from the observed ones by deep neural networks, lacking consideration of the distribution gap between modalities, resulting in the inconsistency of distributions between the recovered and the true data. To mitigate this issue, in this work, we propose a novel recovery paradigm, Distribution-Consistent Modal Recovering (DiCMoR), to transfer the distributions from available modalities to missing modalities, which thus maintains the distribution consistency of recovered data. In particular, we design a class-specific flow based modality recovery method to transform cross-modal distributions on the condition of sample class, which could well predict a distribution-consistent space for missing modality by virtue of the invertibility and exact density estimation of normalizing flow. The generated data from the predicted distribution is integrated with available modalities for the task of classification. Experiments show that DiCMoR gains superior performances and is more robust than existing state-of-the-art methods under various missing patterns. Visualization results show that the distribution gaps between recovered modalities and missing modalities are mitigated. Codes are released at https://***/mdswyz/DiCMoR.

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