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Shuai Feng

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

A Novel Fine-Tuned CLIP-OOD Detection Method with Double Loss Constraint Through Optimal Transport Semantic Alignment

Hengyang Lu
Xin Guo
Shuai Feng
Wenyu Jiang
Yuntao Du
Chang Xia
Chenyou Fan

Detecting Out-Of-Distribution (OOD) samples in image classification is crucial for model reliability. With the rise of Vision-Language Models (VLMs), CLIP-OOD has become a research hotspot. However, we observe the Low Focus Attention phenomenon from the image encoders of CLIP, which means the attention of image encoders often spreads to non-in-distribution regions. This phenomenon comes from the semantic mismalignment and inter-class feature confusion. To address these issues, we propose a novel fine-tuned OOD detection method with the Double loss constraint based on Optimal Transport (DOT-OOD). DOT-OOD integrates the Double Loss Constraint (DLC) module and Optimal Transport (OT) module. The DLC module comprises the Aligned Image-Text Concept Matching Loss and the Negative Sample Repulsion Loss, which respectively (1) focus on the core semantics of ID images and achieve cross-modal semantic alignment, (2) expand inter-class distances and enhance discriminative. While the OT module is introduced to obtain enhanced image feature representations. Extensive experimental results show that in the 16-shot scenario of the ImageNet-1k benchmark, DOT-OOD reduces the FPR95 by over 10% and improves the AUROC from 94.48% to 96.57% compared with SOTAs.

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AAAI Conference 2025 Conference Paper

Robust Logit Adjustment for Learning with Long-Tailed Noisy Data

MingCai Chen
Yuntao Du
Wenyu Jiang
Baoming Zhang
Shuai Feng
Yi Xin
Chongjun Wang

Learning with noisy labels (LNL) methods have enabled the deployment of machine learning systems with imperfectly labeled data. However, these methods often struggle to identify noise in the presence of long-tailed (LT) class distributions, where the memorization effect becomes class-dependent. Conversely, LT methods are suboptimal under label noise, as it hinders access to accurate label frequency statistics. This study aims to address the long-tailed noisy data by bridging the methodological gap between LNL and LT approaches. We propose a direct solution, termed Robust Logit Adjustment, which estimates ground-truth labels through label refurbishment, thereby mitigating the impact of label noise. Simultaneously, our method incorporates the distribution of training-time corrected target labels into the LT method logit adjustment, providing class-rebalanced supervision. Extensive experiments on both synthetic and real-world long-tailed noisy datasets demonstrate the superior performance of our method.

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ICML Conference 2025 Conference Paper

Test-Time Selective Adaptation for Uni-Modal Distribution Shift in Multi-Modal Data

Mingcai Chen
Baoming Zhang
Zongbo Han
Wenyu Jiang
Yanmeng Wang
Shuai Feng
Yuntao Du 0001
Bingkun Bao

Modern machine learning applications are characterized by the increasing size of deep models and the growing diversity of data modalities. This trend underscores the importance of efficiently adapting pre-trained multi-modal models to the test distribution in real time, i. e. , multi-modal test-time adaptation. In practice, the magnitudes of multi-modal shifts vary because multiple data sources interact with the impact factor in diverse manners. In this research, we investigate the the under-explored practical scenario uni-modal distribution shift, where the distribution shift influences only one modality, leaving the others unchanged. Through theoretical and empirical analyses, we demonstrate that the presence of such shift impedes multi-modal fusion and leads to the negative transfer phenomenon in existing test-time adaptation techniques. To flexibly combat this unique shift, we propose a selective adaptation schema that incorporates multiple modality-specific adapters to accommodate potential shifts and a “router” module that determines which modality requires adaptation. Finally, we validate the effectiveness of our proposed method through extensive experimental evaluations. Code available at https: //github. com/chenmc1996/Uni-Modal-Distribution-Shift.

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ICML Conference 2024 Conference Paper

AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors

Yucen Wang
Shenghua Wan
Le Gan
Shuai Feng
De-Chuan Zhan

Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous distractors like noisy background videos, leaving homogeneous distractors that closely resemble controllable agents largely unexplored, which poses significant challenges to existing methods. To tackle this problem, we propose Implicit Action Generator (IAG) to learn the implicit actions of visual distractors, and present a new algorithm named implicit Action-informed Diverse visual Distractors Distinguisher (AD3), that leverages the action inferred by IAG to train separated world models. Implicit actions effectively capture the behavior of background distractors, aiding in distinguishing the task-irrelevant components, and the agent can optimize the policy within the task-relevant state space. Our method achieves superior performance on various visual control tasks featuring both heterogeneous and homogeneous distractors. The indispensable role of implicit actions learned by IAG is also empirically validated.

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AAAI Conference 2024 Conference Paper

CASE: Exploiting Intra-class Compactness and Inter-class Separability of Feature Embeddings for Out-of-Distribution Detection

Shuai Feng
Pengsheng Jin
Chongjun Wang

Detecting out-of-distribution (OOD) inputs is critical for reliable machine learning, but deep neural networks often make overconfident predictions, even for OOD inputs that deviate from the distribution of training data. Prior methods relied on the widely used softmax cross-entropy (CE) loss that is adequate for classifying in-distribution (ID) samples but not optimally designed for OOD detection. To address this issue, we propose CASE, a simple and effective OOD detection method by explicitly improving intra-class Compactness And inter-class Separability of feature Embeddings. To enhance the separation between ID and OOD samples, CASE uses a dual-loss framework, which includes a separability loss that maximizes the inter-class Euclidean distance to promote separability among different class centers, along with a compactness loss that minimizes the intra-class Euclidean distance to encourage samples to be close to their class centers. In particular, the class centers are defined as a free optimization parameter of the model and updated by gradient descent, which is simple and further enhances the OOD detection performance. Extensive experiments demonstrate the superiority of CASE, which reduces the average FPR95 by 37.11% and improves the average AUROC by 15.89% compared to the baseline method using a softmax confidence score on the more challenging CIFAR-100 model.

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NeurIPS Conference 2024 Conference Paper

Leveraging Separated World Model for Exploration in Visually Distracted Environments

Kaichen Huang
Shenghua Wan
Minghao Shao
Hai-Hang Sun
Le Gan
Shuai Feng
De-Chuan Zhan

Model-based unsupervised reinforcement learning (URL) has gained prominence for reducing environment interactions and learning general skills using intrinsic rewards. However, distractors in observations can severely affect intrinsic reward estimation, leading to a biased exploration process, especially in environments with visual inputs like images or videos. To address this challenge, we propose a bi-level optimization framework named Separation-assisted eXplorer (SeeX). In the inner optimization, SeeX trains a separated world model to extract exogenous and endogenous information, minimizing uncertainty to ensure task relevance. In the outer optimization, it learns a policy on imaginary trajectories generated within the endogenous state space to maximize task-relevant uncertainty. Evaluations on multiple locomotion and manipulation tasks demonstrate SeeX's effectiveness.

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ICML Conference 2024 Conference Paper

SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets

Shenghua Wan
Ziyuan Chen
Le Gan
Shuai Feng
De-Chuan Zhan

Model-based offline reinforcement Learning (RL) is a promising approach that leverages existing data effectively in many real-world applications, especially those involving high-dimensional inputs like images and videos. To alleviate the distribution shift issue in offline RL, existing model-based methods heavily rely on the uncertainty of learned dynamics. However, the model uncertainty estimation becomes significantly biased when observations contain complex distractors with non-trivial dynamics. To address this challenge, we propose a new approach - Separated Model-based Offline Policy Optimization (SeMOPO) - decomposing latent states into endogenous and exogenous parts via conservative sampling and estimating model uncertainty on the endogenous states only. We provide a theoretical guarantee of model uncertainty and performance bound of SeMOPO. To assess the efficacy, we construct the Low-Quality Vision Deep Data-Driven Datasets for RL (LQV-D4RL), where the data are collected by non-expert policy and the observations include moving distractors. Experimental results show that our method substantially outperforms all baseline methods, and further analytical experiments validate the critical designs in our method. The project website is https: //sites. google. com/view/semopo.

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AAAI Conference 2023 Conference Paper

READ: Aggregating Reconstruction Error into Out-of-Distribution Detection

Wenyu Jiang
Yuxin Ge
Hao Cheng
MingCai Chen
Shuai Feng
Chongjun Wang

Detecting out-of-distribution (OOD) samples is crucial to the safe deployment of a classifier in the real world. However, deep neural networks are known to be overconfident for abnormal data. Existing works directly design score function by mining the inconsistency from classifier for in-distribution (ID) and OOD. In this paper, we further complement this inconsistency with reconstruction error, based on the assumption that an autoencoder trained on ID data cannot reconstruct OOD as well as ID. We propose a novel method, READ (Reconstruction Error Aggregated Detector), to unify inconsistencies from classifier and autoencoder. Specifically, the reconstruction error of raw pixels is transformed to latent space of classifier. We show that the transformed reconstruction error bridges the semantic gap and inherits detection performance from the original. Moreover, we propose an adjustment strategy to alleviate the overconfidence problem of autoencoder according to a fine-grained characterization of OOD data. Under two scenarios of pre-training and retraining, we respectively present two variants of our method, namely READ-MD (Mahalanobis Distance) only based on pre-trained classifier and READ-ED (Euclidean Distance) which retrains the classifier. Our methods do not require access to test time OOD data for fine-tuning hyperparameters. Finally, we demonstrate the effectiveness of the proposed methods through extensive comparisons with state-of-the-art OOD detection algorithms. On a CIFAR-10 pre-trained WideResNet, our method reduces the average FPR@95TPR by up to 9.8% compared with previous state-of-the-art.

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