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Yiting Li

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization

Jingyi Liao
Yongyi Su
Rong-Cheng Tu
Zhao Jin
Wenhao Sun
Yiting Li
Xun Xu
Dacheng Tao

While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities across diverse domains, their application to specialized anomaly detection (AD) remains constrained by domain adaptation challenges. Existing Group Relative Policy Optimization (GRPO) based approaches suffer from two critical limitations: inadequate training data utilization when models produce uniform responses, and insufficient supervision over reasoning processes that encourage immediate binary decisions without deliberative analysis. We propose a comprehensive framework addressing these limitations through two synergistic innovations. First, we introduce a multi-stage deliberative reasoning process that guides models from region identification to focused examination, generating diverse response patterns essential for GRPO optimization while enabling structured supervision over analytical workflows. Second, we develop a fine-grained reward mechanism incorporating classification accuracy and localization supervision, transforming binary feedback into continuous signals that distinguish genuine analytical insight from spurious correctness. Comprehensive evaluation across multiple industrial datasets shows that our method achieves superior accuracy by enabling general-purpose MLLMs to acquire fine-grained visual discrimination for detecting subtle manufacturing defects.

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YNIMG Journal 2025 Journal Article

Olfactory sensation emotion regulation: The implicit emotion regulation function of positive olfactory stimuli during emotional picture processing

Jiaotao Cai
Xinran Wang
Jiayi Zhou
Ye di
Ziruo Shen
Shuo An
Bingyang Long
Yicheng Wang

Previous research has shown that olfactory stimuli can induce emotional states, physiological and neural responses related to emotions. It remains unclear whether positive olfactory stimuli could down-regulate individuals' negative emotions or up-regulate individuals' positive emotions in an unconscious way. The present study investigated the effects of emotional olfactory stimuli on the behavioral and electrophysiological responses to emotional pictures. Forty participants were exposed to different types of odor conditions (neutral, pleasant, unpleasant) and evaluated the emotional pictures' valence and arousal while electroencephalography was recorded. Behavioral results showed that participants reported more positive emotions in response to positive pictures in two types of pleasant odor conditions (especially the citrus) than in unpleasant or neutral odor conditions. However, the pleasant odor (lavender) increased the positive pictures' valence scores but decreased the positive pictures' arousal scores. The ERPs results showed that the pleasant odors reduced the amplitudes of N1 and EPN components in response to negative pictures, indicating that pleasant odors might down-regulate negative emotions through decreasing attentional capture for negative visual stimuli during the early stage. The pleasant odor (lavender) attenuated LPP amplitudes for emotional pictures, suggesting that the positive olfactory stimuli might be helpful to down-regulate the emotional arousal by reducing attentional deployment to negative visual stimuli during the late stage of emotional visual stimuli processing. These findings provided novel behavioral and neurophysiological evidence that positive olfactory stimuli modulated visual emotional processing across multiple stages, and suggested that olfactory sensation could function as a rapid and relatively effortless emotion regulation modality.

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ICRA Conference 2022 Conference Paper

Incremental Few-Shot Object Detection for Robotics

Yiting Li
Haiyue Zhu
Sichao Tian
Fan Feng
Jun Ma 0008
Chek Sing Teo
Cheng Xiang 0001
Prahlad Vadakkepat

Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated training samples; on the other hand, such new additional tasks should be learned in a continuous and incremental manner without forgetting the previous learned knowledge dramatically. In this work, we propose a novel Class-Incremental Few- Shot Object Detection (CI-FSOD) framework that enables deep object detection network to perform effective continual learning from just few-shot samples without re-accessing the previous training data. We achieve this by equipping the widely-used Faster-RCNN detector with three elegant components. Firstly, to best preserve performance on the pre-trained base classes, we propose a novel Dual-Embedding-Space (DES) architecture which decouples the representation learning of base and novel categories into different spaces. Secondly, to mitigate the catastrophic forgetting on the accumulated novel classes, we propose a Sequential Model Fusion (SMF) method, which is able to achieve long-term memory without additional storage cost. Thirdly, to promote inter-task class separation in feature space, we propose a novel regularization technique that extends the classification boundary further away from the previous classes to avoid misclassification. Overall, our framework is simple yet effective and outperforms the previous SOTA with a significant margin of 2. 4 points in AP performance.

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IROS Conference 2020 Conference Paper

Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation

Haiyue Zhu
Yiting Li
Fengjun Bai
Wenjie Chen
Xiaocong Li
Jun Ma 0008
Chek Sing Teo
Pey Yuen Tao

Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e. g. , in grasping detection, the inference skill learned from a grasping dataset is not universal enough to directly apply on various other daily/industrial applications. This paper presents an approach enabling the easy domain adaptation through a novel grasping detection network with confidence-driven semi-supervised learning, where these two components deeply interact with each other. The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it as the confidence-driven mean teacher. This approach largely prevents the student model to learn the incorrect/harmful information from the consistency loss, which speeds up the learning progress and improves the model accuracy. Our results show that the proposed network can achieve high success rate on the Cornell grasping dataset, and for domain adaptation with very limited data, the confidence- driven mean teacher outperforms the original mean teacher and direct training by more than 10% in evaluation loss especially for avoiding the overfitting and model diverging.

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