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Chenhui Yang

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4 papers

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EAAI Journal 2026 Journal Article

Hybrid semantic segmentation with broad context and attention encoded network for urban street scenario

Khawaja Iftekhar Rashid
Abid Hussain
Chenhui Yang
Chenxi Huang

Real-time semantic segmentation helps intelligent transportation systems analyze dynamic urban environments with precision. So far, even advanced models struggle to capture fine details in live scenarios because they cannot adapt quickly to changing conditions and require heavy computational resources. In this study, we present a hybrid model for semantic segmentation incorporating generative semantic segmentation as a key notch. We present the Dynamic Context-Aware network during the latent prior learning process to capture contextual details. A maskige image is used to generate the segmentation mask for posterior distribution. The conditioning network bridges the gap between the mask's posterior distribution and the latent prior distribution of the training data. Finally, we present the attention-encoded decoder unit to further refine the feature map before the final segmentation map. Rigorous evaluations on established benchmarks confirm that our Hybrid Semantic Segmentation (HSS) method has achieved competitive performance compared to previous state-of-the-art alternatives in the semantic segmentation context. Additionally, our Hybrid Semantic Segmentation system sets a new benchmark in the more challenging real-world urban scenario.

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EAAI Journal 2025 Journal Article

Dynamic context-aware high-resolution network for semi-supervised semantic segmentation

Khawaja Iftekhar Rashid
Chenhui Yang
Chenxi Huang

Real-time semi-supervised semantic segmentation provides a detailed understanding of dynamic urban situations for Intelligent transportation systems. However, state-of-the-art models following different segmentation tasks fail to incorporate High-Resolution Networks (HRNet) for real-time situations due to the inability to adjust in dynamic environments and high computational complexity. The study aims to examine the efficacy of student-teacher networks in semi-supervised semantic segmentation, mainly focusing on intelligent transportation. We present a Dynamic Context-aware High-Resolution Network (DC-HRNet) to enhance semantic segmentation in a dynamic urban environment incorporating a Student-Teacher knowledge distillation mechanism. We train teacher networks utilizing high-resolution networks to enhance robustness and precision in autonomous driving. Additionally, we employ Multi-path Blocks (MPBs) with HRNet to train our student network to address the challenges of the scarcity of labeled datasets in real-world scenarios. Utilization of MPBs for downsampling helps avoid low-resolution loss and generate pseudo labels by leveraging feature maps. We integrate a Dynamic Context-Aware Segmentation Network (DCSNet) to reduce the computational cost further and increase segmentation accuracy. Integrating DCSNet across various resolutions of HRNet with diverse dilation rates has enhanced contextual data utilization. This study contributes to intelligent transportation systems by improving segmentation accuracy, model generalization, and overall resilience, expanding the research scope in this area. Extensive investigations on the publicly available datasets show that our model can handle high-resolution images in real-time with state-of-the-art performance.

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IJCAI Conference 2025 Conference Paper

RDPA: Real-Time Distributed-Concentrated Penetration Attack for Point Cloud Learning

Youtong Shi
Lixin Chen
Yu Zang
Chenhui Yang
Cheng Wang

Partial point attack approaches focus on leveraging the fewest points to achieve the best attack efficiency for easy implementation in the physical domain. For the first time, this paper proposes that the partial point attack strategy should pay attention to not only the selection and disturbance of points, but also the penetration of current defense methods. By re-examining characteristics of previous partial point attack approaches leading to performance improvement, we discover two fundamental principles: first, the selection of attacked points should consider not only the favourable visual salience but also the proper position concentration, thus to acquire effective structural destruction on the basis of remaining imperceptible; second, the perturbation of target points should form meaningful structures rather than outliers. To achieve this, we first propose a novel distributed-concentrated point selection (DPS) strategy, which is easier to concentrate salient points containing rich local information in a few tiny regions. Additionally, to enhance the penetration efficacy and real-time performance of attack point clouds against defenses, we further design a perturbation network based on the multi-scale penetration loss (L_msp), which can generate adversarial samples with as few outliers as possible only through a single forward propagation. Experimental results demonstrate that the real-time distributed-concentrated penetration attack (RDPA) framework can achieve state-of-the-art (SOTA) success rates by perturbing only 3. 5% of points, and have the best penetration for mainstream defense methods such as SRS and SOR.

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AAAI Conference 2019 Short Paper

Geometric Multi-Model Fitting by Deep Reinforcement Learning

Zongliang Zhang
Hongbin Zeng
Jonathan Li
Yiping Chen
Chenhui Yang
Cheng Wang

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e. g. , laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations.

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