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Shaohui Liu

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Context-Aware Hierarchical Learning: A Two-Step Paradigm towards Safer LLMs

Tengyun Ma
Jiaqi Yao
Daojing He
Shihao Peng
Yu Li
Shaohui Liu
Zhuotao Tian

Large Language Models (LLMs) have emerged as powerful tools for diverse applications. However, their uniform token processing paradigm introduces critical vulnerabilities in instruction handling, particularly when exposed to adversarial scenarios. In this work, we identify and propose a novel class of vulnerabilities, termed Tool-Completion Attack (TCA), which exploits function-calling mechanisms to subvert model behavior. To evaluate LLM robustness against such threats, we introduce the Tool-Completion benchmark, a comprehensive security assessment framework, which reveals that even state-of-the-art models remain susceptible to TCA, with surprisingly high attack success rates. To address these vulnerabilities, we introduce Context-Aware Hierarchical Learning (CAHL), a sophisticated mechanism that dynamically equilibrates semantic comprehension with role-specific instruction constraints. CAHL leverages the contextual correlations between different instruction segments to establish a robust, context-aware instruction hierarchy. Extensive experiments demonstrate that CAHL significantly enhances LLM robustness against both conventional attacks and the proposed TCA, exhibiting strong generalization capabilities in zero-shot evaluations while still preserving model performance on generic tasks. Our code is available at https: //github. com/S2AILab/CAHL.

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IROS Conference 2025 Conference Paper

UAV-MaLO: Mamba-Augmented YOLO Hybrid Architecture for UAV Micro-Object Detection in Autonomous Robotics

Lennox Wei
Shixin Sun
Jiaqi Yao
Yachun Mi
Xiangyu Sui
Heng Chen
Shaohui Liu

The rapid advancement of drone technology has led to the widespread application of micro-object detection in Unmanned Aerial Vehicle (UAV) systems. However, with the constraint of real-time computation, critical challenges remain in addressing extreme scale variations, low-resolution signatures and dense occlusions. For object detection task, although YOLO-based detectors outperform transformer models in efficiency-accuracy balance, their limited capacity for global context modeling and feature discriminability in complex aerial environments hinders optimal performance. To overcome these limitations, we introduce UAV-MaLO, a novel framework that incorporates state space modeling principles into YOLO’s architecture. By introducing the abilities of long-range dependency modeling and adaptive spatial-frequency fusion, the proposed approach dynamically optimizes receptive fields while suppressing background interference, achieving robust micro-object localization in cluttered scenarios. Furthermore, the parallelized attention mechanism and the hierarchical feature refinement further ensure real-time processing capabilities without compromising detection precision, establishing a new paradigm for UAV deployment. Our experimental results on the VisDrone-2019-DET dataset reveal a significant improvement in various variants of average precision (AP), indicating the extraordinary performance of our UAV-MaLO.

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NeurIPS Conference 2024 Conference Paper

AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos

Yuze He
Wang Zhao
Shaohui Liu
Yubin Hu
Yushi Bai
Yu-Hui Wen
Yong-Jin Liu

We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation. By representing 3D planes as rectangles with alpha channels, AlphaTablets combine the advantages of current 2D and 3D plane representations, enabling accurate, consistent and flexible modeling of 3D planes. We derive differentiable rasterization on top of AlphaTablets to efficiently render 3D planes into images, and propose a novel bottom-up pipeline for 3D planar reconstruction from monocular videos. Starting with 2D superpixels and geometric cues from pre-trained models, we initialize 3D planes as AlphaTablets and optimize them via differentiable rendering. An effective merging scheme is introduced to facilitate the growth and refinement of AlphaTablets. Through iterative optimization and merging, we reconstruct complete and accurate 3D planes with solid surfaces and clear boundaries. Extensive experiments on the ScanNet dataset demonstrate state-of-the-art performance in 3D planar reconstruction, underscoring the great potential of AlphaTablets as a generic 3D plane representation for various applications.

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EAAI Journal 2018 Journal Article

Hyperspectral classification based on spectral–spatial convolutional neural networks

Congcong Chen
Feng Jiang
Chifu Yang
Seungmin Rho
Weizheng Shen
Shaohui Liu
Zhiguo Liu

Hyperspectral image classification is an important task in remote sensing image analysis. Traditional machine learning techniques are difficult to deal with hyperspectral images directly, because hyperspectral images have too many redundant spectral channels. In this paper we propose a novel method for hyperspectral image classification, by which spectral and spatial features are jointly exploited from hyperspectral images. Firstly, considering the local similarity in spatial domain, we employ a large spatial window to get image blocks from hyperspectral image Secondly, each spectral channel of the image block is filtered to extract their spatial and spectral features, after that the features are merged by convolutional layers. Finally, the fully-connected layers are used to get the classification result. Comparing with other state-of-the-art techniques, the proposed method pays more attention to the correlation of spatial neighborhood by using a large spatial window in the network. In addition, we combine the proposed network with the traditional support vector machine (SVM) classifier to improve the performance of hyperspectral image classification. Moreover, an adaptive method of the spatial window sizes selection is proposed in this paper. Experimental results conducted on the AVIRIS and ROSIS datasets demonstrate that the proposed method outperforms the state-of-the-art techniques.

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TIST Journal 2012 Journal Article

Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding

Shengping Zhang
Hongxun Yao
Xin Sun
Shaohui Liu

Intelligent video surveillance is currently one of the most active research topics in computer vision, especially when facing the explosion of video data captured by a large number of surveillance cameras. As a key step of an intelligent surveillance system, robust visual tracking is very challenging for computer vision. However, it is a basic functionality of the human visual system (HVS). Psychophysical findings have shown that the receptive fields of simple cells in the visual cortex can be characterized as being spatially localized, oriented, and bandpass, and it forms a sparse, distributed representation of natural images. In this article, motivated by these findings, we propose an effective appearance model based on sparse coding and apply it in visual tracking. Specifically, we consider the responses of general basis functions extracted by independent component analysis on a large set of natural image patches as features and model the appearance of the tracked target as the probability distribution of these features. In order to make the tracker more robust to partial occlusion, camouflage environments, pose changes, and illumination changes, we further select features that are related to the target based on an entropy-gain criterion and ignore those that are not. The target is finally represented by the probability distribution of those related features. The target search is performed by minimizing the Matusita distance between the distributions of the target model and a candidate using Newton-style iterations. The experimental results validate that the proposed method is more robust and effective than three state-of-the-art methods.

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