Wen Yang

AAAI Conference 2025 Conference Paper

Asymmetric Hierarchical Difference-aware Interaction Network for Event-guided Motion Deblurring

• Wen Yang
• Jinjian Wu
• Leida Li
• Weisheng Dong
• Guangming Shi

Event cameras are bio-inspired sensors that are capable of capturing motion information with high temporal resolution, which show potential in aiding image motion deblurring recently. Most existing methods indiscriminately handle feature fusion of two modalities with symmetric unidirectional/bidirectional interactions at different-level layers in feature encoder, while ignoring the different dependencies between cross-modal hierarchical features. To tackle these limitations, we propose a novel Asymmetric Hierarchical Difference-aware Interaction Network (AHDINet) for event-based motion deblurring, which explores the complementarity of two modalities with differential dependence modeling of cross-modal hierarchical features. Thereby, an event-assisted edge complement module is designed to leverage event modality to enhance the edge details of the image features in low-level encoder stage, and an image-assisted semantic complement module is developed to transfer contextual semantics of image features to event branch in high-level encoder stage. Benefiting from the proposed differentiated interaction mode, the respective advantages of image and event modalities are fully exploited. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance.

EAAI Journal 2025 Journal Article

Defense against false data injection attacks on the electric vehicle charging stations data markets

• Huqun Mu
• Aiping Pang
• Congmei Jiang
• Wen Yang
• Qianchuan Zhao

IROS Conference 2025 Conference Paper

Differential-Flatness-Based Tracking Control for Tractor-Trailers in Reversing Maneuvers

• Bo Yang 0064
• Zhenhao Zhuang
• Zitian Yu
• Qian Wang
• Junqing Wei
• Yilin Mo
• Wen Yang

In this paper, we propose a differential-flatness-based controller (DFBC) for precise trajectory tracking of tractor-trailers, particularly during reversing maneuvers, which are challenging due to unstable equilibrium points. The proposed controller leverages the differential flatness property of tractor-trailers, equivalently transforming the nonlinear kinematics into a brunovsky canonical form, allowing the application of linear control theory for control design. Compared to traditional linear quadratic regulator (LQR) controllers, the proposed DFBC method achieves higher precision and robustness in reversing maneuvers. We also showcase the performance of the proposed DFBC method through physical experiments conducted on our self-developed 1/10 scale autonomous tractor-trailer.

NeurIPS Conference 2025 Conference Paper

KTAE: A Model-Free Algorithm to Key-Tokens Advantage Estimation in Mathematical Reasoning

• Wei Sun
• Wen Yang
• Pu Jian
• Qianlong Du
• Fuwei Cui
• Shuo Ren
• Jiajun Zhang

Recent advances have demonstrated that integrating reinforcement learning with rule-based rewards can significantly enhance the reasoning capabilities of large language models (LLMs), even without supervised fine-tuning (SFT). However, prevalent reinforcement learning algorithms such as GRPO and its variants like DAPO, suffer from a coarse granularity issue when computing the advantage. Specifically, they compute rollout-level advantages that assign identical values to every token within a sequence, failing to capture token-specific contributions. To address this limitation, we propose Key-token Advantage Estimation (KTAE)—a novel algorithm that estimates fine-grained, token-level advantages without introducing additional models. KTAE leverages the correctness of sampled rollouts and applies statistical analysis to quantify the importance of individual tokens within a sequence to the final outcome. This quantified token-level importance is then combined with the rollout-level advantage to obtain a more fine-grained token-level advantage estimation. Empirical results show that models trained with GRPO+KTAE and DAPO+KTAE outperform baseline methods across five mathematical reasoning benchmarks. Notably, they achieve higher accuracy with shorter responses and even surpass R1-Distill-Qwen-1. 5B using the same base model.

ICLR Conference 2025 Conference Paper

Language Imbalance Driven Rewarding for Multilingual Self-improving

• Wen Yang
• Junhong Wu
• Chen Wang
• Chengqing Zong
• Jiajun Zhang 0001

Large Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks. However, these advancements have predominantly benefited "first-class" languages such as English and Chinese, leaving many other languages underrepresented. This imbalance, while limiting broader applications, generates a natural preference ranking between languages, offering an opportunity to bootstrap the multilingual capabilities of LLM in a self-improving manner. Thus, we propose $\textit{Language Imbalance Driven Rewarding}$, where the inherent imbalance between dominant and non-dominant languages within LLMs is leveraged as a reward signal. Iterative DPO training demonstrates that this approach not only enhances LLM performance in non-dominant languages but also improves the dominant language's capacity, thereby yielding an iterative reward signal. Fine-tuning Meta-Llama-3-8B-Instruct over two iterations of this approach results in continuous improvements in multilingual performance across instruction-following and arithmetic reasoning tasks, evidenced by an average improvement of 7.46\% win rate on the X-AlpacaEval leaderboard and 13.9\% accuracy on the MGSM benchmark. This work serves as an initial exploration, paving the way for multilingual self-improvement of LLMs.

EAAI Journal 2024 Journal Article

Another way: Direct regression of meter readings for circular pointer meter images

• Dongsheng Ji
• Wenbo Zhang
• Wen Yang
• Qianchuan Zhao

AAAI Conference 2024 Conference Paper

Motion Deblurring via Spatial-Temporal Collaboration of Frames and Events

• Wen Yang
• Jinjian Wu
• Jupo Ma
• Leida Li
• Guangming Shi

Motion deblurring can be advanced by exploiting informative features from supplementary sensors such as event cameras, which can capture rich motion information asynchronously with high temporal resolution. Existing event-based motion deblurring methods neither consider the modality redundancy in spatial fusion nor temporal cooperation between events and frames. To tackle these limitations, a novel spatial-temporal collaboration network (STCNet) is proposed for event-based motion deblurring. Firstly, we propose a differential-modality based cross-modal calibration strategy to suppress redundancy for complementarity enhancement, and then bimodal spatial fusion is achieved with an elaborate cross-modal co-attention mechanism to weight the contributions of them for importance balance. Besides, we present a frame-event mutual spatio-temporal attention scheme to alleviate the errors of relying only on frames to compute cross-temporal similarities when the motion blur is significant, and then the spatio-temporal features from both frames and events are aggregated with the custom cross-temporal coordinate attention. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance. Project website: https://github.com/wyang-vis/STCNet.