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Yanan Li

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

Ambiguity-Tolerant Cross-Modal Hashing with Partial Labels

Chao Su
Yanan Li
Xu Wang
Yingke Chen
Huiming Zheng
Dezhong Peng
Yuan Sun

Cross-modal hashing (CMH) has achieved remarkable success in large-scale cross-modal retrieval due to its low storage cost and high computational efficiency. However, most existing CMH methods rely on accurately annotated training data, which is often impractical in real-world applications due to the high cost and limited scalability of data annotation. In practice, annotators typically assign a candidate label set rather than a single precise label to each sample pair, resulting in partial labels with inherent ambiguity. Such ambiguous supervision poses significant challenges to conventional CMH methods that assume reliable and unambiguous labels. In this paper, we investigate a less-touched yet meaningful problem, i.e., cross-modal hashing with partial labels (PLCMH). PLCMH faces two major challenges: label ambiguity and modality-alignment barriers induced by misleading supervision. To address these issues, we propose a new approach named Ambiguity-Tolerant Cross-Modal Hashing (ATCH). Specifically, ATCH presents a Local Consensus Disambiguation (LCD) mechanism that resolves label ambiguity by effectively inferring stable and accurate label confidence based on local consensus within the Hamming space. Moreover, ATCH proposes a Confidence-Aware Contrastive Hashing (CACH) mechanism that derives both pseudo labels and trustworthiness scores from the label confidence vectors to learn discriminative hash codes, leading to effective modality alignment. Extensive experiments on three multimodal datasets demonstrate the superiority of ATCH.

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JBHI Journal 2026 Journal Article

Diagnosis of Major Depressive Disorder Based on Multi-Granularity Brain Networks Fusion

Mengni Zhou
Rongkun Mi
Ang Zhao
Xin Wen
Yan Niu
Xubin Wu
Yanqing Dong
Yaru Xu

Major Depressive Disorder (MDD) is a common mental disorder, and making an early and accurate diagnosis is crucial for effective treatment. Functional Connectivity Network (FCN) constructed based on functional Magnetic Resonance Imaging (fMRI) have demonstrated the potential to reveal the mechanisms underlying brain abnormalities. Deep learning has been widely employed to extract features from FCN, but existing methods typically operate directly on the network, failing to fully exploit their deep information. Although graph coarsening techniques offer certain advantages in extracting the brain’s complex structure, they may also result in the loss of critical information. To address this issue, we propose the Multi-Granularity Brain Networks Fusion (MGBNF) framework. MGBNF models brain networks through multi-granularity analysis and constructs combinatorial modules to enhance feature extraction. Finally, the Constrained Attention Pooling (CAP) mechanism is employed to achieve the effective integration of multi-channel features. In the feature extraction stage, the parameter sharing mechanism is introduced and applied to multiple channels to capture similar connectivity patterns between different channels while reducing the number of parameters. We validate the effectiveness of the MGBNF model on multiple classification tasks and various brain atlases. The results demonstrate that MGBNF outperforms baseline models in terms of classification performance. Ablation experiments further validate its effectiveness. In addition, we conducted a thorough analysis of the variability of different subtypes of MDD by multiple classification tasks, and the results support further clinical applications.

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YNIMG Journal 2026 Journal Article

Hierarchical neurobehavioral model reveals that shared flexibility, not individual stability, supports rhythmic coordination

Ruoyu Niu
Yanan Li
Lei Liu
Yafeng Pan
Ying Liu

Interpersonal coordination requires balancing individual control with interaction-derived synergy, yet it remains unclear when neural coupling contributes beyond behavior. Using an fNIRS hyperscanning paradigm, we examined dyadic rhythmic coordination and jointly modeled behavioral stability, dispositional structure, and interbrain synchrony within a hierarchical neurobehavioral framework. Across models, mean individual stability was negatively associated with dyadic performance, whereas interaction-derived shared flexibility (i.e., dyad-level behavioral stability synergy) was the most robust positive predictor. Incorporating dispositional structure showed that larger within-dyad differences in figure-embedding performance impaired coordination, whereas higher dyad-level self-esteem facilitated coordination. The neural coupling index showed no reliable main effect after accounting for behavioral and trait factors, but moderation analyses indicated a conditional contribution: interbrain synchrony compensated when shared flexibility was low, with diminishing benefit as synergy increased. Together, these findings support a hierarchical neurobehavioral architecture in which shared flexibility provides the primary foundation of coordination, dispositional structure shapes the conditions for synergy, and interbrain synchrony contributes in a context-dependent manner.

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JBHI Journal 2026 Journal Article

Towards Unconstrained Fall Detection Using Vision Language Model: Dataset, Theory and Practices

Shiman Wu
Tianyi Chen
Zhihao Zha
Bin Wu
Yixin Li
Ran Wang
Yanan Li
Chong Tian

Unconstrained fall detection is essential for real-world applications. However, it remains underexplored due to the scarcity of real-world fall data and the limited generalization ability of existing methods. To address these challenges, we first introduce HUST-FALL, a fine-grained text-video dataset for unconstrained fall detection, featuring diverse fall scenarios and rich semantic annotations. Building on this dataset, we propose Action-R1, a lightweight vision-language model that leverages structured textual guidance and reasoning to improve the understanding of fall events. In challenging cross-dataset tests, Action-R1 achieves an average F1 score of 0. 827 on three benchmarks, significantly outperforming conventional CNN/RNN-based methods. Despite having only 1/16 the parameters, Action-R1 achieves competitive performance against MiniCPM-V 2. 6, even surpassing it on UPFall by 116. 22%. These results demonstrate that Action-R1 is a lightweight yet powerful solution for unconstrained fall detection in real-world scenarios.

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NeurIPS Conference 2025 Conference Paper

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades

Yanan Li
Fanxu Meng
Muhan Zhang
Shiai Zhu
Shangguang Wang
Mengwei Xu

As Large Language Models (LLMs) are frequently updated, LoRA weights trained on earlier versions quickly become obsolete. The conventional practice of retraining LoRA weights from scratch on the latest model is costly, time-consuming, and environmentally detrimental, particularly as the diversity of LLMs and downstream tasks expands. This motivates a critical question: "How can we efficiently leverage existing LoRA weights to adapt to newer model versions? " To address this, we propose LoRASuite, a modular approach tailored specifically to various types of LLM updates. First, we compute a transfer matrix utilizing known parameters from both old and new LLMs. Next, we allocate corresponding layers and attention heads based on centered kernel alignment and cosine similarity metrics, respectively. A subsequent small-scale, skillful fine-tuning step ensures numerical stability. Experimental evaluations demonstrate that LoRASuite consistently surpasses small-scale vanilla LoRA methods. Notably, on backbone LLMs such as MiniCPM and Qwen, LoRASuite even exceeds the performance of full-scale LoRA retraining, with average improvements of +1. 4 and +6. 6 points on math tasks, respectively. Additionally, LoRASuite significantly reduces memory consumption by 5. 5 GB and computational time by 78. 23%.

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NeurIPS Conference 2023 Conference Paper

A High-Resolution Dataset for Instance Detection with Multi-View Object Capture

Qianqian Shen
Yunhan Zhao
Nahyun Kwon
Jeeeun Kim
Yanan Li
Shu Kong

Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that current InsDet datasets are too small in scale by today's standards. For example, the popular InsDet dataset GMU (published in 2016) has only 23 instances, far less than COCO (80 classes), a well-known object detection dataset published in 2014. We are motivated to introduce a new InsDet dataset and protocol. First, we define a realistic setup for InsDet: training data consists of multi-view instance captures, along with diverse scene images allowing synthesizing training images by pasting instance images on them with free box annotations. Second, we release a real-world database, which contains multi-view capture of 100 object instances, and high-resolution (6k$\times$8k) testing images. Third, we extensively study baseline methods for InsDet on our dataset, analyze their performance and suggest future work. Somewhat surprisingly, using the off-the-shelf class-agnostic segmentation model (Segment Anything Model, SAM) and the self-supervised feature representation DINOv2 performs the best, achieving $>$10 AP better than end-to-end trained InsDet models that repurpose object detectors (e. g. , FasterRCNN and RetinaNet).

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IROS Conference 2023 Conference Paper

Online Estimation of 2D Human Arm Stiffness for Peg-in-Hole Tasks with Variable Impedance Control

Huayang Wu
Hanwen Yang
Yanan Li

This paper proposes an online estimation model for 2D arm stiffness in humans. The proposed model is based on recent physiological findings which suggest that: (1) joint stiffness is linearly related to the magnitude of joint torque and increases to compensate for environmental disturbances; and (2) the endpoint stiffness of the arm is proportional to grasp force. To validate the proposed model, perturbation experiments were conducted under different grasp forces. The model parameters were identified and the accuracy of the model was assessed. The results showed that the proposed model has advantages over previous models for estimating human arm endpoint stiffness, in the sense of simplicity and robustness. The proposed model was also used to design a variable stiffness controller for peg-in-hole tasks, demonstrating the potential of the model for human-robot collaboration.

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NeurIPS Conference 2022 Conference Paper

Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid

Jing Xu
Xu Luo
Xinglin Pan
Yanan Li
Wenjie Pei
Zenglin Xu

Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning methods, the sample selection bias problem, i. e. , the sensitivity to the limited amount of support data, has not been well understood. In this paper, we find that this problem usually occurs when the positions of support samples are in the vicinity of task centroid—the mean of all class centroids in the task. This motivates us to propose an extremely simple feature transformation to alleviate this problem, dubbed Task Centroid Projection Removing (TCPR). TCPR is applied directly to all image features in a given task, aiming at removing the dimension of features along the direction of the task centroid. While the exact task centoid cannot be accurately obtained from limited data, we estimate it using base features that are each similar to one of the support features. Our method effectively prevents features from being too close to the task centroid. Extensive experiments over ten datasets from different domains show that TCPR can reliably improve classification accuracy across various feature extractors, training algorithms and datasets. The code has been made available at https: //github. com/KikimorMay/FSL-TCBR.

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EAAI Journal 2022 Journal Article

An intelligent quality-based fusion method for complex-valued distributions using POWA operator

Yanan Li
Ruonan Zhu
Xiangjun Mi
Bingyi Kang

Information quality (IQ) is a significant index in information processing, e. g. multiple information fusion. An extended method considering the IQ has been investigated using the multi-complex-valued information. However, how to fuse the multiple complex-valued distributions (CvDs) with correlation and preference of decision-maker may be a fantastic issue. In this paper, we propose a new method using the power ordered weighted average (POWA) operator to integrate multi-complex-valued distributions considering the information correlation and preference of decision-maker. The proposed method is an extension of the previous work. Some examples and applications are used to illustrate the effectiveness of the proposed method.

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AAAI Conference 2021 Conference Paper

Inference Fusion with Associative Semantics for Unseen Object Detection

Yanan Li
Pengyang Li
Han Cui
Donghui Wang

We study the problem of object detection when training and test objects are disjoint, i. e. no training examples of the target classes are available. Existing unseen object detection approaches usually combine generic detection frameworks with a single-path unseen classifier, by aligning object regions with semantic class embeddings. In this paper, inspired from human cognitive experience, we propose a simple but effective dual-path detection model that further explores associative semantics to supplement the basic visual-semantic knowledge transfer. We use a novel target-centric multipleassociation strategy to establish concept associations, to ensure that the predictor generalized to unseen domain can be learned during training. In this way, through a reasonable inference fusion mechanism, those two parallel reasoning paths can strengthen the correlation between seen and unseen objects, thus improving detection performance. Experiments show that our inductive method can significantly boost the performance by 7. 42% over inductive models, and even 5. 25% over transductive models on MSCOCO dataset.

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IJCAI Conference 2020 Conference Paper

Dress like an Internet Celebrity: Fashion Retrieval in Videos

Hongrui Zhao
Jin Yu
Yanan Li
Donghui Wang
Jie Liu
Hongxia Yang
Fei Wu

Nowadays, both online shopping and video sharing have grown exponentially. Although internet celebrities in videos are ideal exhibition for fashion corporations to sell their products, audiences do not always know where to buy fashion products in videos, which is a cross-domain problem called video-to-shop. In this paper, we propose a novel deep neural network, called Detect, Pick, and Retrieval Network (DPRNet), to break the gap between fashion products from videos and audiences. For the video side, we have modified the traditional object detector, which automatically picks out the best object proposals for every commodity in videos without duplication, to promote the performance of the video-to-shop task. For the fashion retrieval side, a simple but effective multi-task loss network obtains new state-of-the-art results on DeepFashion. Extensive experiments conducted on a new large-scale cross-domain video-to-shop dataset shows that DPRNet is efficient and outperforms the state-of-the-art methods on video-to-shop task.

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AAAI Conference 2016 Conference Paper

Relational Knowledge Transfer for Zero-Shot Learning

Donghui Wang
Yanan Li
Yuetan Lin
Yueting Zhuang

General zero-shot learning (ZSL) approaches exploit transfer learning via semantic knowledge space. In this paper, we reveal a novel relational knowledge transfer (RKT) mechanism for ZSL, which is simple, generic and effective. RKT resolves the inherent semantic shift problem existing in ZSL through restoring the missing manifold structure of unseen categories via optimizing semantic mapping. It extracts the relational knowledge from data manifold structure in semantic knowledge space based on sparse coding theory. The extracted knowledge is then transferred backwards to generate virtual data for unseen categories in the feature space. On the one hand, the generalizing ability of the semantic mapping function can be enhanced with the added data. On the other hand, the mapping function for unseen categories can be learned directly from only these generated data, achieving inspiring performance. Incorporated with RKT, even simple baseline methods can achieve good results. Extensive experiments on three challenging datasets show prominent performance obtained by RKT, and we obtain 82. 43% accuracy on the Animals with Attributes dataset.

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