Hui Li

Shuangchen Zhao, Changde Du, Hui Li et al.

Deep Neural Networks (DNNs) have demonstrated exceptional recognition capabilities in traditional computer vision (CV) tasks. However, existing CV models often suffer a significant decrease in accuracy when confronted with out-of-distribution (OOD) data. In contrast to these DNN models, human can maintain a consistently low error rate when facing OOD scenes, partly attributed to the rich prior cognitive knowledge stored in the human brain. Previous OOD generalization researches only focus on the single modal, overlooking the advantages of multimodal learning method. In this paper, we utilize the multimodal learning method to improve the OOD generalization and propose a novel Brain-machine Fusion Learning (BMFL) framework. We adopt the cross-attention mechanism to fuse the visual knowledge from CV model and prior cognitive knowledge from the human brain. Specially, we employ a pre-trained visual neural encoding model to predict the functional Magnetic Resonance Imaging (fMRI) from visual features which eliminates the need for the fMRI data collection and pre-processing, effectively reduces the workload associated with conventional BMFL methods. Furthermore, we construct a brain transformer to facilitate the extraction of knowledge inside the fMRI data. Moreover, we introduce the Pearson correlation coefficient maximization regularization method into the training process, which improves the fusion capability with better constrains. Our model outperforms the DINOv2 and baseline models on the ImageNet-1k validation dataset as well as six curated OOD datasets, showcasing its superior performance in diverse scenarios.

Qian Zhang, Panfeng Chen, Jiali Li et al.

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pediatrics. Moreover, some of them are limited to objective questions and do not measure the generation capacity of LLMs. Therefore, they cannot comprehensively assess the QA ability of LLMs in pediatrics. To fill this gap, we construct PediaBench, the first Chinese pediatric dataset for LLM evaluation. Specifically, it contains 4,117 objective questions and 1,632 subjective questions spanning 12 pediatric disease groups. It adopts an integrated scoring criterion based on different difficulty levels to thoroughly assess the proficiency of an LLM in instruction following, knowledge understanding, clinical case analysis, etc. Finally, we validate the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs. Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. Our code and data are published at https://github.com/ACMISLab/PediaBench.

He Kong, Die Hu, Jingguo Ge et al.

Automating penetration testing is crucial for enhancing cybersecurity, yet current Large Language Models (LLMs) face significant limitations in this domain, including poor error handling, inefficient reasoning, and an inability to perform complex end-to-end tasks autonomously. To address these challenges, we introduce Pentest-R1, a novel framework designed to optimize LLM reasoning capabilities for this task through a two-stage reinforcement learning pipeline. We first construct a dataset of over 500 real-world, multi-step walkthroughs, which Pentest-R1 leverages for offline reinforcement learning (RL) to instill foundational attack logic. Subsequently, the LLM is fine-tuned via online RL in an interactive Capture The Flag (CTF) environment, where it learns directly from environmental feedback to develop robust error self-correction and adaptive strategies. Our extensive experiments on the Cybench and AutoPenBench benchmarks demonstrate the framework's effectiveness. On AutoPenBench, Pentest-R1 achieves a 24.2\% success rate, surpassing most state-of-the-art models and ranking second only to Gemini 2.5 Flash. On Cybench, it attains a 15.0\% success rate in unguided tasks, establishing a new state-of-the-art for open-source LLMs and matching the performance of top proprietary models. Ablation studies confirm that the synergy of both training stages is critical to its success.

Fujian Yan, Hui Li, Hongsheng He

Object affordance and volumetric information are essential in devising effective grasping strategies under task-specific constraints. This paper presents an approach for inferring suitable grasping strategies from limited partial views of an object. To achieve this, a recurrent generative adversarial network (R-GAN) was proposed by incorporating a recurrent generator with long short-term memory (LSTM) units for it to process a variable number of depth scans. To determine object affordances, the AffordPose knowledge dataset is utilized as prior knowledge. Affordance retrieving is defined by the volume similarity measured via Chamfer Distance and action similarities. A Proximal Policy Optimization (PPO) reinforcement learning model is further implemented to refine the retrieved grasp strategies for task-oriented grasping. The retrieved grasp strategies were evaluated on a dual-arm mobile manipulation robot with an overall grasping accuracy of 89% for four tasks: lift, handle grasp, wrap grasp, and press.

Weihong Ma, Hesuo Zhang, Shuang Yan et al.

In this paper, we fill the research gap by adopting state-of-the-art computer vision techniques for the data extraction stage in a data mining system. As shown in Fig.1, this stage contains two subtasks, namely, plot element detection and data conversion. For building a robust box detector, we comprehensively compare different deep learning-based methods and find a suitable method to detect box with high precision. For building a robust point detector, a fully convolutional network with feature fusion module is adopted, which can distinguish close points compared to traditional methods. The proposed system can effectively handle various chart data without making heuristic assumptions. For data conversion, we translate the detected element into data with semantic value. A network is proposed to measure feature similarities between legends and detected elements in the legend matching phase. Furthermore, we provide a baseline on the competition of Harvesting raw tables from Infographics. Some key factors have been found to improve the performance of each stage. Experimental results demonstrate the effectiveness of the proposed system.