Author name cluster

Fei Yang

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Linguistic Steganography via Self-Adjusting Asymmetric Number System (Abstract Reprint)

Yiting Liu
Chungen Xu
Fei Yang
Pan Zhang
Linlong Wang

Linguistic steganography (stego) seeks to conceal secret information within natural language text. However, existing methods often struggle to balance stego text quality with embedding efficiency, largely due to limitations in generation strategies and coding mechanisms. We propose SA-ANS, a self-adaptive linguistic steganography framework based on a self-adjusting Asymmetric Numeral System. SA-ANS allows user-specified embedding rates and uses probabilistic coding with adaptive candidate selection, dynamically tailoring the token pool to the language model’s probability distribution. This design produces fluent, semantically coherent stego text while preserving statistical indistinguishability from natural language. Extensive experiments on multiple benchmark datasets, evaluated across embedding efficiency, linguistic quality, statistical similarity, robustness to steganalysis, and human judgment, show that SA-ANS consistently outperforms state-of-the-art methods, demonstrating both effectiveness and practicality.

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EAAI Journal 2025 Journal Article

Adaptive detection method for driver fatigue using facial multisource dynamic behavior fusion

Guoxin Zhang
Fei Yang
Xin Fang
Lili Wang
Lei Zhao
Chaoning Yu

Driving while fatigued is a leading cause of traffic accidents. This study proposed an adaptive detection model to recognize driver fatigue based on the dynamic facial behavior information of drivers. First, drivers’ facial fatigue features were extracted to establish a general feature space, including pupil movement, eye state, and fatigue expression parameters. A differentiated feature space was then built based on individual drivers, taking into account the homogeneity, regularity, and individual variances in drivers' facial behavior at various states. A complete adaptive fatigue feature space was built by integrating the general feature space and differentiated feature space. Finally, a driver adaptive fatigue discrimination model was constructed to classify the general and adaptive fatigue feature space to detect driver fatigue states adaptively. A driver fatigue detection dataset from real scenarios had been established to validate the performance of the proposed model. Experimental results demonstrated that the proposed method significantly improved the detection accuracy of driver fatigue. In terms of artificial intelligence, this study contributes a novel adaptive feature space construction method based on multimodal dynamic feature fusion for facial fatigue recognition; in engineering application, it develops an adaptive driver fatigue detection system grounded in multimodal dynamic behaviors, which provides real-time alerts upon detecting driver fatigue and ensures driving safety.

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ICML Conference 2025 Conference Paper

BSLoRA: Enhancing the Parameter Efficiency of LoRA with Intra-Layer and Inter-Layer Sharing

Yuhua Zhou
Ruifeng Li
Changhai Zhou
Fei Yang
Aimin Pan

Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning method for large language models (LLMs) to adapt to downstream tasks. However, in scenarios where multiple LoRA models are deployed simultaneously, standard LoRA introduces substantial trainable parameters, resulting in significant memory overhead and inference latency, particularly when supporting thousands of downstream tasks on a single server. While existing methods reduce stored parameters via parameter sharing, they fail to capture both local and global information simultaneously. To address this issue, we propose the Bi-Share LoRA (BSLoRA), which extends local LoRA with intra-LoRA and inter-LoRA parameter sharing to better capture local and global information. This approach reduces trainable parameters while maintaining or even enhancing model performance. Additionally, we design three transformation methods to improve the compatibility and collaborative efficiency of shared parameters with varying shapes, enhancing overall adaptability. Experiments on the 7B, 8B, and 13B versions of Llama show that BSLoRA, with only 44. 59% of the parameters of standard LoRA, outperforms LoRA by approximately 0. 33% on commonsense reasoning and 2. 08% on MMLU benchmarks. Code is available at https: //github. com/yuhua-zhou/BSLoRA. git.

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JBHI Journal 2025 Journal Article

Geometric Deep Learning for Protein-Ligand Affinity Prediction with Hybrid Message Passing Strategies

Jiaren Li
Huasen Jiang
Wenjian Ma
Xiangpeng Bi
Rui Chen
Weigang Lu
Qing Cai
Fei Yang

Accurate prediction of protein-ligand affinity (PLA) is critical for drug discovery. Recent deep learning approaches have adopted data-driven models for PLA prediction by learning intrinsic patterns from one-dimensional (1D) sequential or two-dimensional (2D) graph representations of proteins and ligands. However, these low-dimensional methods overlook the three-dimensional (3D) geometric features, which are hypothesized to be critical in binding interaction. To address the above problem, we present a Geo metric deep learning approach with Hybrid message passing strategies- HybridGeo, for protein-ligand affinity prediction. We adopt dual-view graph learning to model the intra- and inter-molecular atomic interactions and propose to aggregate the spatial information with hybrid strategies. In addition, to fully model the inter-residue dependency upon message aggregation, we adopt a geometric graph transformer on the residue-scale graph of protein pockets. Extensive experiments on the PDBbind dataset show that HybridGeo achieves state-of-the-art performance with a Root Mean Square Error (RMSE) of 1. 172. HybridGeo also achieves the best among all baseline models on three external test sets, showcasing good generalizability and robustness. Through systematic ablation experiments, we validated the effectiveness of the proposed modules, and further demonstrated the superior performance of HybridGeo in predicting the binding affinity of macrocyclic compound complexes through case studies. Visualization analysis further indicates the biological interpretability of the model predictions. Our code is publicly available at https://github.com/anxiangbiye1231/HybridGeo

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NeurIPS Conference 2025 Conference Paper

Improving Video Generation with Human Feedback

Jie Liu
Gongye Liu
Jiajun Liang
Ziyang Yuan
Xiaokun Liu
Mingwu Zheng
Xiele Wu
Qiulin Wang

Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this work, we develop a systematic pipeline that harnesses human feedback to mitigate these problems and refine the video generation model. Specifically, we begin by constructing a large-scale human preference dataset focused on modern video generation models, incorporating pairwise annotations across multi-dimensions. We then introduce VideoReward, a multi-dimensional video reward model, and examine how annotations and various design choices impact its rewarding efficacy. From a unified reinforcement learning perspective aimed at maximizing reward with KL regularization, we introduce three alignment algorithms for flow-based models. These include two training-time strategies: direct preference optimization for flow (Flow-DPO) and reward weighted regression for flow (Flow-RWR), and an inference-time technique, Flow-NRG, which applies reward guidance directly to noisy videos. Experimental results indicate that VideoReward significantly outperforms existing reward models, and Flow-DPO demonstrates superior performance compared to both Flow-RWR and supervised fine-tuning methods. Additionally, Flow-NRG lets users assign custom weights to multiple objectives during inference, meeting personalized video quality needs.

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NeurIPS Conference 2025 Conference Paper

Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning

Xusheng Cao
Haori Lu
Linlan Huang
Fei Yang
Xialei Liu
Ming-Ming Cheng

Continual learning in computer vision faces the critical challenge of catastrophic forgetting, where models struggle to retain prior knowledge while adapting to new tasks. Although recent studies have attempted to leverage the generalization capabilities of pre-trained models to mitigate overfitting on current tasks, models still tend to forget details of previously learned categories as tasks progress, leading to misclassification. To address these limitations, we introduce a novel Knowledge Graph Enhanced Generative Multi-modal model (KG-GMM) that builds an evolving knowledge graph throughout the learning process. Our approach utilizes relationships within the knowledge graph to augment the class labels and assigns different relations to similar categories to enhance model differentiation. During testing, we propose a Knowledge Graph Augmented Inference method that locates specific categories by analyzing relationships within the generated text, thereby reducing the loss of detailed information about old classes when learning new knowledge and alleviating forgetting. Experiments demonstrate that our method effectively leverages relational information to help the model correct mispredictions, achieving state-of-the-art results in both conventional CIL and few-shot CIL settings, confirming the efficacy of knowledge graphs at preserving knowledge in the continual learning scenarios.

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EAAI Journal 2023 Journal Article

A novel seminar learning framework for weakly supervised salient object detection

Yan Liu
Yunzhou Zhang
Zhenyu Wang
Fei Yang
Feng Qiu
Sonya Coleman
Dermot Kerr

Weakly supervised salient object detection (SOD) is a challenging task and has drawn much attention from several research perspectives, it has revealed two problems while driving the rapid development of saliency detection. (1) Large divergence in the characteristics of saliency regions in terms of location, shape and size makes them difficult to recognize. (2) The properties of convolutional neural networks dictate that it is insensitive to various transformations, which will lead to hardly balance the application of various disturbances. To tackle these limitations, this paper proposes a novel seminar learning framework with consistent transformation ensembling (SLF-CT) for scribble supervised SOD. The framework consists of the teacher–student model and the student–student model for segmenting the salient objects. Specifically, we first design a cross attention guided network (CAGNet) as a baseline model for saliency prediction. Then we assign CAGNet to the teacher–student model, where the teacher network is based on the exponential moving average and guides the training of the student network. Moreover, we adopt multiple pseudo labels to transfer the information among students from different conditions. To further enhance the regularization of the network, a consistency transformation mechanism is also incorporated, which encourages the saliency prediction and input image of the network to be consistent. The experimental results demonstrate that the proposed approach performs favorably comparable with the state-of-the-art weakly supervised methods. As far as we know, the proposed approach is the first application of seminar learning in the SOD area.

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NeurIPS Conference 2023 Conference Paper

Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing

Kai Wang
Fei Yang
Shiqi Yang
Muhammad Atif Butt
Joost van de Weijer

Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are susceptible to unintended modifications of regions outside the targeted area, such as on the background or on distractor objects which have some semantic or visual relationship with the targeted object. According to our experimental findings, inaccurate cross-attention maps are at the root of this problem. Based on this observation, we propose $\textit{Dynamic Prompt Learning}$ ($DPL$) to force cross-attention maps to focus on correct $\textit{noun}$ words in the text prompt. By updating the dynamic tokens for nouns in the textual input with the proposed leakage repairment losses, we achieve fine-grained image editing over particular objects while preventing undesired changes to other image regions. Our method $DPL$, based on the publicly available $\textit{Stable Diffusion}$, is extensively evaluated on a wide range of images, and consistently obtains superior results both quantitatively (CLIP score, Structure-Dist) and qualitatively (on user-evaluation). We show improved prompt editing results for Word-Swap, Prompt Refinement, and Attention Re-weighting, especially for complex multi-object scenes.

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NeurIPS Conference 2022 Conference Paper

Exploring evolution-aware & -free protein language models as protein function predictors

Mingyang Hu
Fajie Yuan
Kevin Yang
Fusong Ju
Jin Su
Hui Wang
Fei Yang
Qiuyang Ding

Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold, Evoformer, has not been explored beyond structure prediction. In this paper, we investigate the representation ability of three popular PLMs: ESM-1b (single sequence), MSA-Transformer (multiple sequence alignment), and Evoformer (structural), with a special focus on Evoformer. Specifically, we aim to answer the following key questions: (1) Does the Evoformer trained as part of AlphaFold produce representations amenable to predicting protein function? (2) If yes, can Evoformer replace ESM-1b and MSA-Transformer? (3) How much do these PLMs rely on evolution-related protein data? In this regard, are they complementary to each other? We compare these models by empirical study along with new insights and conclusions. All code and datasets for reproducibility are available at https: //github. com/elttaes/Revisiting-PLMs.

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