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Tan Yue

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

MARS: Multimodal Adaptive Reasoning Model for Avoiding Overthinking

Tan Yue
Qiong Wu
Dongyan Zhao

Multimodal Large Language Models (MLLMs) have shown advanced performance in vision-language tasks. However, existing multimodal reasoning models often suffer from excessive reasoning steps, leading to high computational costs and inefficiency. In this paper, we propose the Multimodal Adaptive Reasoning Model (MARS), which enables adaptive adjustment of the reasoning strategy based on question difficulty. Specifically, MARS adopts a three-stage training framework based on our constructed training dataset (MART): 1) CoT Masking Learning to enhance reasoning logicality by predicting masked reasoning steps. 2) Adaptive Reasoning Instruction Learning to train the model to skip or keep reasoning steps according to difficulty levels. 3) CoT Lightweight Reinforcement Learning with the Information Bottleneck Principle based GRPO algorithm to reduce CoT length while maintaining performance and generalizability. Results on both in-domain and out-of-domain datasets show that MARS significantly reduces the CoT length (90.2% decrease) while improving accuracy (0.54%), outperforming existing SOTA open-source and proprietary MLLMs.

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IS Journal 2025 Journal Article

ARISE: Explainable Multimodal Aggressive Driving Detection via Driver State and Environment Perception

Sainan Zhang
Jun Zhang
Weiguo Song
Tan Yue
Luyao Zhu

Detecting aggressive driving is challenging but crucial for public safety. Existing methods rely on time-series data of drivers’ physiology, behavior, and vehicle movement but overlook driver’s emotion and environmental influences. We propose ARISE, a multisource aggregation model integrating physiological, behavioral, and emotional data, vehicle sensor inputs, and environmental conditions. ARISE employs multisource feature extraction, multimodal fusion, and a classifier to detect aggressive driving. Unlike graph-based methods that fails to detect gradual aggression shifts or transformer-based methods prone to delays, ARISE explicitly models vehicle state continuity and the aggressive driving environment. Motion similarity descriptor tracks state transitions, while aggression descriptor quantifies environmental aggression. Additionally, a driving performance descriptor assesses driving workload and stability. Experiments show that ARISE significantly outperforms state-of-the-art methods in aggressive driving detection.

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ICLR Conference 2023 Conference Paper

Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings

Heng Wang
Tan Yue
Xiang Ye
Zihang He
Bohan Li 0013
Yong Li 0025

Few-Shot Learning (FSL) aims to learn a simple and effective bias on limited novel samples. Recently, many methods have been focused on re-training a randomly initialized linear classifier to adapt it to the novel features extracted by the pre-trained feature extractor (called Linear-Probing-based methods). These methods typically assumed the pre-trained feature extractor was robust enough, i.e., finetuning was not needed, and hence the pre-trained feature extractor does not change on the novel samples. However, the unchanged pre-trained feature extractor will distort the features of novel samples because the robustness assumption may not hold, especially on the out-of-distribution samples. To extract the undistorted features, we designed Linear-Probing-Finetuning with Firth-Bias (LP-FT-FB) to yield an accurate bias on the limited samples for better finetuning the pre-trained feature extractor, providing stronger transferring ability. In LP-FT-FB, we further proposed inverse Firth Bias Reduction (i-FBR) to regularize the over-parameterized feature extractor on which FBR does not work well. The proposed i-FBR effectively alleviates the over-fitting problem of the feature extractor in the process of finetuning and helps extract undistorted novel features. To show the effectiveness of the designed LP-FT-FB, we conducted a lot of experiments on the commonly used FSL datasets under different backbones, including in-domain and cross-domain FSL tasks. The experimental results show that the proposed FT-LP-FB outperforms the SOTA FSL methods. The code is available at https://github.com/whzyf951620/LinearProbingFinetuningFirthBias.

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