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Weichen Yu

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7 papers

2 author rows

EAAI Journal 2026 Journal Article

Weight prediction of the oxidation film in aircraft aluminium alloy components with small samples using data augmentation and random forest

Shuai Li
Zhuo Yu
Yudong Chen
Jiaqi Mai
Xiaofeng Zhou
Weichen Yu
Yigeng Wang

Anodic oxidation stands as one of the pivotal processes in the surface modification of aircraft aluminum alloy components. The weight of the oxidation film typically exerts an influence on the comprehensive performance of the components, which also significantly impacts the service life of diverse aircrafts. Nevertheless, the intricate coupling characteristics stemming from multiple process parameters and small sample sizes present formidable challenges to the weight prediction of the oxidation film. In response to these issues, this study develops a weight prediction method of oxidation film using data augmentation and random forest (RF). Initially, given the scarcity of oxidation film weight data, this study designs a data augmentation method using quadratic B-spline interpolation and generative adversarial network (GAN) to augment the quantity of data and enhance representational capabilities. Subsequently, to assess the quality of the augmented data, a comprehensive evaluation index (CEI) using mean squared error (MSE) and Kullback-Leibler (KL) divergence is presented. Finally, considering complex coupling characteristics of process parameters, a weight prediction model using attention mechanism (AM) and RF is built to enhance the prediction performance. The results of data augmentation and oxidation film weight prediction in the actual anodic oxidation process of aircraft aluminum alloy component demonstate the feasibility and effectiveness.

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NeurIPS Conference 2025 Conference Paper

GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detection

Xin Gao
Jiyao Liu
Guanghao Li
Yueming LYU
Jianxiong Gao
Weichen Yu
Ningsheng Xu
Liang Wang

Recent advancements have explored text-to-image diffusion models for synthesizing out-of-distribution (OOD) samples, substantially enhancing the performance of OOD detection. However, existing approaches typically rely on perturbing text-conditioned embeddings, resulting in semantic instability and insufficient shift diversity, which limit generalization to realistic OOD. To address these challenges, we propose GOOD, a novel and flexible framework that directly guides diffusion sampling trajectories towards OOD regions using off-the-shelf in-distribution (ID) classifiers. GOOD incorporates dual-level guidance: (1) Image-level guidance based on the gradient of log partition to reduce input likelihood, drives samples toward low-density regions in pixel space. (2) Feature-level guidance, derived from k-NN distance in the classifier’s latent space, promotes sampling in feature-sparse regions. Hence, this dual-guidance design enables more controllable and diverse OOD sample generation. Additionally, we introduce a unified OOD score that adaptively combines image and feature discrepancies, enhancing detection robustness. We perform thorough quantitative and qualitative analyses to evaluate the effectiveness of GOOD, demonstrating that training with samples generated by GOOD can notably enhance OOD detection performance.

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AAAI Conference 2025 Conference Paper

Learning Fine-Grained Alignment for Aerial Vision-Dialog Navigation

Yifei Su
Dong An
Kehan Chen
Weichen Yu
Baiyang Ning
Yonggen Ling
Yan Huang
Liang Wang

Aerial Vision-Dialog Navigation (AVDN) is a new task that requires drones to navigate to a target location based on human-robot dialog history. This paper focuses on the critical fine-grained cross-modal alignment problem in AVDN, requiring the drone to align language entities with visual landmarks in top-down views. To achieve this, we first construct a Fine-Grained AVDN (FG-AVDN) dataset via a semi-automatic annotation pipeline, providing diverse multimodal annotations at the entity-landmark level. Based on this, a novel Fine-grained Entity-Landmark Alignment (FELA) method is proposed to learn the cross-modal alignment explicitly. Concretely, FELA first boosts the drone's visual understanding with a precise semantic grid representation, which captures the environmental semantics and spatial structure simultaneously. Subsequently, to learn the entity-landmark alignment, we devise cross-modal auxiliary tasks from three perspectives, including grounding, captioning, and contrastive learning. Extensive experiments demonstrate that our explicit entity-landmark alignment learning is beneficial for AVDN. As a result, FELA achieves leading performance with 3.2% SR and 4.9% GP improvements over prior arts. Code and dataset will be publicly available.

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NeurIPS Conference 2024 Conference Paper

Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization

Kai Hu
Weichen Yu
Yining Li
Tianjun Yao
Xiang Li
Wenhe Liu
Lijun Yu
Zhiqiang Shen

Recent research indicates that large language models (LLMs) are susceptible to jailbreaking attacks that can generate harmful content. This paper introduces a novel token-level attack method, Adaptive Dense-to-Sparse Constrained Optimization (ADC), which has been shown to successfully jailbreak multiple open-source LLMs. Drawing inspiration from the difficulties of discrete token optimization, our method relaxes the discrete jailbreak optimization into a continuous optimization process while gradually increasing the sparsity of the optimizing vectors. This technique effectively bridges the gap between discrete and continuous space optimization. Experimental results demonstrate that our method is more effective and efficient than state-of-the-art token-level methods. On Harmbench, our approach achieves the highest attack success rate on seven out of eight LLMs compared to the latest jailbreak methods. \textcolor{red}{Trigger Warning: This paper contains model behavior that can be offensive in nature. }

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NeurIPS Conference 2024 Conference Paper

Metric from Human: Zero-shot Monocular Metric Depth Estimation via Test-time Adaptation

Yizhou Zhao
Hengwei Bian
Kaihua Chen
Pengliang Ji
Liao Qu
Shao-yu Lin
Weichen Yu
Haoran Li

Monocular depth estimation (MDE) is fundamental for deriving 3D scene structures from 2D images. While state-of-the-art monocular relative depth estimation (MRDE) excels in estimating relative depths for in-the-wild images, current monocular metric depth estimation (MMDE) approaches still face challenges in handling unseen scenes. Since MMDE can be viewed as the composition of MRDE and metric scale recovery, we attribute this difficulty to scene dependency, where MMDE models rely on scenes observed during supervised training for predicting scene scales during inference. To address this issue, we propose to use humans as landmarks for distilling scene-independent metric scale priors from generative painting models. Our approach, Metric from Human (MfH), bridges from generalizable MRDE to zero-shot MMDE in a generate-and-estimate manner. Specifically, MfH generates humans on the input image with generative painting and estimates human dimensions with an off-the-shelf human mesh recovery (HMR) model. Based on MRDE predictions, it propagates the metric information from painted humans to the contexts, resulting in metric depth estimations for the original input. Through this annotation-free test-time adaptation, MfH achieves superior zero-shot performance in MMDE, demonstrating its strong generalization ability.

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ICML Conference 2023 Conference Paper

Bag of Tricks for Training Data Extraction from Language Models

Weichen Yu
Tianyu Pang
Qian Liu 0033
Chao Du
Bingyi Kang
Yan Huang
Min Lin
Shuicheng Yan

With the advance of language models, privacy protection is receiving more attention. Training data extraction is therefore of great importance, as it can serve as a potential tool to assess privacy leakage. However, due to the difficulty of this task, most of the existing methods are proof-of-concept and still not effective enough. In this paper, we investigate and benchmark tricks for improving training data extraction using a publicly available dataset. Because most existing extraction methods use a pipeline of generating-then-ranking, i. e. , generating text candidates as potential training data and then ranking them based on specific criteria, our research focuses on the tricks for both text generation (e. g. , sampling strategy) and text ranking (e. g. , token-level criteria). The experimental results show that several previously overlooked tricks can be crucial to the success of training data extraction. Based on the GPT-Neo 1. 3B evaluation results, our proposed tricks outperform the baseline by a large margin in most cases, providing a much stronger baseline for future research. The code is available at https: //github. com/weichen-yu/LM-Extraction.

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IJCAI Conference 2022 Conference Paper

Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting

Hongyuan Yu
Ting Li
Weichen Yu
Jianguo Li
Yan Huang
Liang Wang
Alex Liu

Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, most existing works focus more on learning with the explicit prior graph structure, while ignoring potential information from the implicit graph structure, yielding incomplete structure modeling. Some recent works attempts to learn the intrinsic or implicit graph structure directly, while lacking a way to combine explicit prior structure with implicit structure together. In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure. RGSL consists of two innovative modules. First, we derive an implicit dense similarity matrix through node embedding, and learn the sparse graph structure using the Regularized Graph Generation (RGG) based on the Gumbel Softmax trick. Second, we propose a Laplacian Matrix Mixed-up Module (LM3) to fuse the explicit graph and implicit graph together. We conduct experiments on three real-word datasets. Results show that the proposed RGSL model outperforms existing graph forecasting algorithms with a notable margin, while learning meaningful graph structure simultaneously. Our code and models are made publicly available at https: //github. com/alipay/RGSL. git.

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