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Xiaobo Jin

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4 papers

2 author rows

EAAI Journal 2025 Journal Article

Progressive Enhancement Dehazing for object detection in extreme weather

Zhiying Li
Junhao Wu
Shuyuan Lin
Zheng Wang
Xiaobo Jin
Guanggang Geng
Feiran Huang
Jian Weng

Existing object detection technologies have made significant advances in perception systems for autonomous driving. However, accurately detecting objects in extreme weather conditions, especially fog, remains a significant challenge. On the one hand, current methods struggle to balance image enhancement and object detection, leading to the neglect of essential information that could improve detection accuracy. On the other hand, the lack of mature datasets in this area limits many studies to synthetic fog data generated from the prior-based atmospheric scattering model, which inevitably restricts further performance improvements. To address these challenges, we propose the Progressive Enhancement Dehazing You Only Look Once (PED-YOLO) method, which processes images in real time under foggy conditions to improve object detection. We design a novel progressive image processing module that follows a unique paradigm of progressive supervised learning, which gradually processes the image from small size to large size, to effectively overcome the challenge of processing large-sized images at once. Moreover, we develop a small convolutional network module that focuses on each channel of the image and enables more accurate adaptive prediction of the parameters of the filters. In addition, we develop a novel style transfer model to generate simulated fog images that more closely resemble real fog images and train them together to bring the synthetic fog domain closer to the real fog domain and improve the generalizability. We evaluate our method extensively on several popular datasets, and the experimental results show the superior performance of PED-YOLO, highlighting its potential to advance autonomous driving.

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AAAI Conference 2025 Conference Paper

Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem Generation

Xiaoqiang Kang
Zimu Wang
Xiaobo Jin
Wei Wang
Kaizhu Huang
Qiufeng Wang

Solving tabular math word problems (TMWPs) has become a critical role in evaluating the mathematical reasoning ability of large language models (LLMs), where large-scale TMWP samples are commonly required for fine-tuning. Since the collection of high-quality TMWP datasets is costly and time-consuming, recent research has concentrated on automatic TMWP generation. However, current generated samples usually suffer from issues of either correctness or diversity. In this paper, we propose a Template-driven LLM-paraphrased (TeLL) framework for generating high-quality TMWP samples with diverse backgrounds and accurate tables, questions, answers, and solutions. To this end, we first extract templates from existing real samples to generate initial problems, ensuring correctness. Then, we adopt an LLM to extend templates and paraphrase problems, obtaining diverse TMWP samples. Furthermore, we find the reasoning annotation is important for solving TMWPs. Therefore, we propose to enrich each solution with illustrative reasoning steps. Through the proposed framework, we construct a high-quality dataset TabMWP-TeLL by adhering to the question types in the TabMWP dataset, and we conduct extensive experiments on a variety of LLMs to demonstrate the effectiveness of TabMWP-TeLL in improving TMWP-solving performance.

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ICLR Conference 2025 Conference Paper

ZeroDiff: Solidified Visual-semantic Correlation in Zero-Shot Learning

Zihan Ye
Shreyank N. Gowda
Shiming Chen 0002
Xiaowei Huang 0001
Haotian Xu
Fahad Shahbaz Khan
Yaochu Jin
Kaizhu Huang

Zero-shot Learning (ZSL) aims to enable classifiers to identify unseen classes. This is typically achieved by generating visual features for unseen classes based on learned visual-semantic correlations from seen classes. However, most current generative approaches heavily rely on having a sufficient number of samples from seen classes. Our study reveals that a scarcity of seen class samples results in a marked decrease in performance across many generative ZSL techniques. We argue, quantify, and empirically demonstrate that this decline is largely attributable to spurious visual-semantic correlations. To address this issue, we introduce ZeroDiff, an innovative generative framework for ZSL that incorporates diffusion mechanisms and contrastive representations to enhance visual-semantic correlations. ZeroDiff comprises three key components: (1) Diffusion augmentation, which naturally transforms limited data into an expanded set of noised data to mitigate generative model overfitting; (2) Supervised-contrastive (SC)-based representations that dynamically characterize each limited sample to support visual feature generation; and (3) Multiple feature discriminators employing a Wasserstein-distance-based mutual learning approach, evaluating generated features from various perspectives, including pre-defined semantics, SC-based representations, and the diffusion process. Extensive experiments on three popular ZSL benchmarks demonstrate that ZeroDiff not only achieves significant improvements over existing ZSL methods but also maintains robust performance even with scarce training data. Our codes are available at https://github.com/FouriYe/ZeroDiff_ICLR25.

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ECAI Conference 2024 Conference Paper

Target-driven Attack for Large Language Models

Chong Zhang 0006
Mingyu Jin
Dong Shu
Taowen Wang
Dongfang Liu
Xiaobo Jin

Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions through the user interface, thus causing LLM model security challenges like the language model not giving the correct answer. Although there is currently a large amount of research on black-box attacks, most of these black-box attacks use random and heuristic strategies. It is unclear how these strategies relate to the success rate of attacks and thus effectively improve model robustness. To solve this problem, we propose our target-driven black-box attack method to maximize the KL divergence between the conditional probabilities of the clean text and the attack text to redefine the attack’s goal. We transform the distance maximization problem into two convex optimization problems based on the attack goal to solve the attack text and estimate the covariance. Furthermore, the projected gradient descent algorithm solves the vector corresponding to the attack text. Our target-driven black-box attack approach includes two attack strategies: token manipulation and misinformation attack. Experimental results on multiple Large Language Models and datasets demonstrate the effectiveness of our attack method.

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