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Pengyu Li

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Progressive Multi-modal Knowledge Distillation for Multi-spectral Object Re-identification

Aihua Zheng
Pengyu Li
Zi Wang
Jin Tang

In the field of multi-spectral object re-identification (ReID), multi-modal knowledge and modal-specific knowledge exhibit complementary advantages when handling hard samples, but existing methods rarely integrate this collaborative information. Knowledge distillation is a direct approach for transferring information, however, heterogeneity in model architectures and variations in sample hardness can undermine the stability and controllability of knowledge transfer. To alleviate these limitations, we propose the novel Progressive Multi-modal Knowledge Distillation (PMKD) framework that enables multi-stage knowledge transfer guided by hard sample awareness. In the multi-modal knowledge transfer stage, the source model (pre-trained on multi-modal data) disseminates its learned multi-modal collaborative knowledge to multiple independently modal-specific target models, guiding their adaptation to hard samples within training batches. In the modal-specific knowledge retention stage, the independent models enriched with multi-modal knowledge guide the training phase. The architectural consistency between source-target models ensures more lossless knowledge transfer, effectively mitigating the risk of capability drift, and preserving inherent competence. Moreover, the entire progressive multi-modal knowledge distillation is regulated by the proposed hardness-aware distillation loss, which automatically adapts distillation intensity through hard sample mining, thereby ensuring stable transfer of hard sample handling capabilities. Extensive experiments on benchmark multi-spectral ReID datasets validate the effectiveness and superior performance of the proposed method.

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ICML Conference 2024 Conference Paper

Neural Collapse in Multi-label Learning with Pick-all-label Loss

Pengyu Li
Xiao Li
Yutong Wang 0002
Qing Qu 0001

We study deep neural networks for the multi-label classification (MLab) task through the lens of neural collapse (NC). Previous works have been restricted to the multi-class classification setting and discovered a prevalent NC phenomenon comprising of the following properties for the last-layer features: (i) the variability of features within every class collapses to zero, (ii) the set of feature means form an equi-angular tight frame (ETF), and (iii) the last layer classifiers collapse to the feature mean upon some scaling. We generalize the study to multi-label learning, and prove for the first time that a generalized NC phenomenon holds with the "pick-all-label” formulation, which we term as MLab NC. While the ETF geometry remains consistent for features with a single label, multi-label scenarios introduce a unique combinatorial aspect we term the "tag-wise average" property, where the means of features with multiple labels are the scaled averages of means for single-label instances. Theoretically, under proper assumptions on the features, we establish that the only global optimizer of the pick-all-label cross-entropy loss satisfy the multi-label NC. In practice, we demonstrate that our findings can lead to better test performance with more efficient training techniques for MLab learning.

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AAAI Conference 2023 Conference Paper

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

Kaicheng Li
Hongyu Yang
Binghui Chen
Pengyu Li
Biao Wang
Di Huang

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results.

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AAAI Conference 2021 Conference Paper

Adversarial Pose Regression Network for Pose-Invariant Face Recognitions

Pengyu Li
Biao Wang
Lei Zhang

Face recognition has achieved significant progress in recent years. However, the large pose variation between face images remains a challenge in face recognition. We observe that the pose variation in the hidden feature maps is one of the most critical factors to hinder the representations from being pose-invariant. Based on the observation, we propose an Adversarial Pose Regression Network (APRN) to extract poseinvariant identity representations by disentangling their pose variation in hidden feature maps. To model the pose discriminator in APRN as a regression task in its 3D space, we also propose an Adversarial Regression Loss Function and extend the adversarial learning from classification problems to regression problems in this paper. Our APRN is a plug-andplay structure that can be embedded in other state-of-the-art face recognition algorithms to improve their performance additionally. The experiments show that the proposed APRN consistently and significantly boosts the performance of baseline networks without extra computational costs in the inference phase. APRN achieves comparable or even superior to the state-of-the-art on CFP, Multi-PIE, IJB-A and MegaFace datasets. The code will be released1, hoping to nourish our proposals to other computer vision fields.

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AAAI Conference 2021 Conference Paper

Deep Metric Learning with Graph Consistency

Binghui Chen
Pengyu Li
Zhaoyi Yan
Biao Wang
Lei Zhang

Deep Metric Learning (DML) has been more attractive and widely applied in many computer vision tasks, in which a discriminative embedding is requested such that the image features belonging to the same class are gathered together and the ones belonging to different classes are pushed apart. Most existing works insist to learn this discriminative embedding by either devising powerful pair-based loss functions or hardsample mining strategies. However, in this paper, we start from another perspective and propose Deep Consistent Graph Metric Learning (CGML) framework to enhance the discrimination of the learned embedding. It is mainly achieved by rethinking the conventional distance constraints as a graph regularization and then introducing a Graph Consistency regularization term, which intends to optimize the feature distribution from a global graph perspective. Inspired by the characteristic of our defined ’Discriminative Graph’, which regards DML from another novel perspective, the Graph Consistency regularization term encourages the sub-graphs randomly sampled from the training set to be consistent. We show that our CGML indeed serves as an efficient technique for learning towards discriminative embedding and is applicable to various popular metric objectives, e. g. Triplet, N-Pair and Binomial losses. This paper empirically and experimentally demonstrates the effectiveness of our graph regularization idea, achieving competitive results on the popular CUB, CARS, Stanford Online Products and In-Shop datasets.

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AAAI Conference 2018 Conference Paper

Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network

Yi Luo
Guojie Song
Pengyu Li
Zhongang Qi

Medical concept normalization is a critical problem in biomedical research and clinical applications. In this paper, we focus on normalizing diagnostic and procedure names in Chinese discharge summaries to standard entities, which is formulated as a semantic matching problem. However, non-standard Chinese expressions, short-text normalization and heterogeneity of tasks pose critical challenges in our problem. This paper presents a general framework which introduces a tensor generator and a novel multi-view convolutional neural network (CNN) with multi-task shared structure to tackle the two tasks simultaneously. We propose that the key to address non-standard expressions and short-text problem is to incorporate a matching tensor with multiple granularities. Then multi-view CNN is adopted to extract semantic matching patterns and learn to synthesize them from different views. Finally, multi-task shared structure allows the model to exploit medical correlations between disease and procedure names to better perform disambiguation tasks. Comprehensive experimental analysis indicates our model outperforms existing baselines which demonstrates the effectiveness of our model.

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