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Tian Yang

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3 papers

2 author rows

JBHI Journal 2026 Journal Article

Multi-Task Learning for OSA Detection and Sleep Staging via Multi-Scale Modeling

Zhiya Wang
Tian Yang
Yunfeng Zhu
Jia Liu
Peter A. Cistulli
Wei Chen

Obstructive sleep apnea (OSA) and sleep fragmentation are closely linked physiological phenomena that play crucial roles in the diagnosis and management of sleep disorders. While numerous deep learning models have been developed for either OSA detection or sleep stage classification, few attempts have been made to address both tasks simultaneously. To this end, we propose MT-TASPPNet (Multi-Task Triple Atrous Spatial Pyramid Pooling Network), a unified multi-modal multi-task network that jointly performs automatic OSA event detection and sleep staging. The model integrates modality-specific feature extractors for EEG, ECG, and airflow signals, and employs Atrous Spatial Pyramid Pooling modules in both the modality-specific and shared representation pathways to capture multi-scale temporal-frequency patterns. Additionally, an EOG-guided prior mechanism is incorporated to enhance the discrimination of subtle sleep stages. We use a 3-min input window (1-min target with ±1-min context) and evaluate our method on three large-scale datasets: SHHS1, SHHS2, and Sydney Sleep Biobank. The model achieves OSA detection accuracy between 0. 798 and 0. 884 (MF1: 0. 772 to 0. 821), and sleep staging accuracy between 0. 776 and 0. 834 (MF1: 0. 735 to 0. 749, $\mathcal {K}$: 0. 697 to 0. 77). Notably, the model maintains consistent performance despite data heterogeneity and individual variability. These results validate the stability and adaptability of MT-TASPPNet in clinical settings, paving the way for efficient and scalable multi-task sleep analysis systems.

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IROS Conference 2025 Conference Paper

Adjacent-view Transformers for Supervised Surround-view Depth Estimation

Xianda Guo
Wenjie Yuan
Yunpeng Zhang
Tian Yang
Chenming Zhang
Zheng Zhu
Qin Zou 0001
Long Chen 0005

Depth estimation has been widely studied and serves as the fundamental step of 3D perception for robotics and autonomous driving. Though significant progress has been made in monocular depth estimation in the past decades, these attempts are mainly conducted on the KITTI benchmark with only front-view cameras, which ignores the correlations across surround-view cameras. In this paper, we propose an Adjacent-View Transformer for Supervised Surround-view Depth estimation (AVT-SSDepth), to jointly predict the depth maps across multiple surrounding cameras. Specifically, we employ a global-to-local feature extraction module that combines CNN with transformer layers for enriched representations. Further, the adjacent-view attention mechanism is proposed to enable the intra-view and inter-view feature propagation. The former is achieved by the self-attention module within each view, while the latter is realized by the adjacent attention module, which computes the attention across multi-cameras to exchange the multi-scale representations across surround-view feature maps. In addition, AVT-SSDepth has strong cross-dataset generalization. Extensive experiments show that our method achieves superior performance over existing state-of-the-art methods on both DDAD and nuScenes datasets. Code is available at https://github.com/XiandaGuo/SSDepth.

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ICRA Conference 2023 Conference Paper

HFT: Lifting Perspective Representations via Hybrid Feature Transformation for BEV Perception

Jiayu Zou
Zheng Zhu
Junjie Huang 0005
Tian Yang
Guan Huang 0003
Xingang Wang 0003

Restoring an accurate Bird's Eye View (BEV) map plays a crucial role in the perception of autonomous driving. The existing works of lifting representations from frontal view to BEV can be classified into two categories, i. e. , Camera model-Based Feature Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT). We empirically analyze the significant differences between CBFT and CFFT. The former method lift perspective features based on the flat- world assumption, which often causes distortion of regions lying above the ground plane. The latter method is limited in the perception performance due to the absence of geometric priors and time-consuming computing. In this paper, we propose a novel framework with a Hybrid Feature Transformation module (HFT) to lift perspective representations. Furthermore, we design a mutual learning scheme to augment hybrid transformation. The deformable attention mechanism enables the model to pay more attention to relevant regions and capture features with more semantics. We illustrate the effectiveness of HFT in BEV perception tasks, such as segmentation and object detection. Notably, in the task of semantic segmentation, extensive experiments demonstrate that HFT outperforms the previous state-of-the-art method by relatively 17. 9% on the Argoverse and 22. 0% on the KITTI 3D Object dataset. With negligible computing budget, HFT outperforms existing image- based methods on 3D object detection. The code will be released soon.

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