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Bin Sheng

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10 papers
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10

JBHI Journal 2025 Journal Article

MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging

  • Ruhan Liu
  • Jiajia Li
  • Yang Wen
  • Xian Huang
  • Bin Sheng
  • David Dagan Feng
  • Ping Zhang

Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global temporal information. Second, the correlation for special waves in different signals is rarely used in sleep staging modeling. Third, the logic of scoring rules based on adjacent epochs is not considered in developing sleep staging models. This paper introduces a multimodal hybrid-embedding fusion network (MHFNet), which aims to tackle these challenges in automating sleep stage scoring. MHFNet comprises multi-stream Xception blocks to extract wave characteristics, a hybrid time-embedding module to combine local and global temporal information, a dual-path gate transformer to fuse and enhance attention features, and a refined output header to reconstruct sleep scoring. We perform experiments using three publicly available datasets (SleepEDF-ST, SleepEDF-SC, and SHHS). Experimental results indicate the superiority of MHFNet over baseline approaches in cross-validation. Moreover, at the individual level, MHFNet yielded an average $R^{2}$ score improvement of 9 $\%$ in the testing dataset compared to state-of-the-art models, paving the way for its applications in real-world sleep medicine.

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AAAI Conference 2024 Conference Paper

Text2City: One-Stage Text-Driven Urban Layout Regeneration

  • Yiming Qin
  • Nanxuan Zhao
  • Bin Sheng
  • Rynson W.H. Lau

Regenerating urban layout is an essential process for urban regeneration. In this paper, we propose a new task called text-driven urban layout regeneration, which provides an intuitive input modal - text - for users to specify the regeneration, instead of designing complex rules. Given the target region to be regenerated, we propose a one-stage text-driven urban layout regeneration model, Text2City, to jointly and progressively regenerate the urban layout (i.e., road and building layouts) based on textual layout descriptions and surrounding context (i.e., urban layouts and functions of the surrounding regions). Text2City first extracts road and building attributes from the textual layout description to guide the regeneration. It includes a novel one-stage joint regenerator network based on the conditioned denoising diffusion probabilistic models (DDPMs) and prior knowledge exchange. To harmonize the regenerated layouts through joint optimization, we propose the interactive & enhanced guidance module for self-enhancement and prior knowledge exchange between road and building layouts during the regeneration. We also design a series of constraints from attribute-, geometry- and pixel-levels to ensure rational urban layout generation. To train our model, we build a large-scale dataset containing urban layouts and layout descriptions, covering 147K regions. Qualitative and quantitative evaluations show that our proposed method outperforms the baseline methods in regenerating desirable urban layouts that meet the textual descriptions.

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AAAI Conference 2023 Short Paper

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection (Student Abstract)

  • Haoyang Chen
  • Shuai Liu
  • Feng Lu
  • Wei Li
  • Bin Sheng
  • Mi Li
  • Hai Jin
  • Albert Y. Zomaya

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly.

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AAAI Conference 2022 Conference Paper

Input-Specific Robustness Certification for Randomized Smoothing

  • Ruoxin Chen
  • Jie Li
  • Junchi Yan
  • Ping Li
  • Bin Sheng

Although randomized smoothing has demonstrated high certified robustness and superior scalability to other certified defenses, the high computational overhead of the robustness certification bottlenecks the practical applicability, as it depends heavily on the large sample approximation for estimating the confidence interval. In existing works, the sample size for the confidence interval is universally set and agnostic to the input for prediction. This Input-Agnostic Sampling (IAS) scheme may yield a poor Average Certified Radius (ACR)-runtime trade-off which calls for improvement. In this paper, we propose Input-Specific Sampling (ISS) acceleration to achieve the cost-effectiveness for robustness certification, in an adaptive way of reducing the sampling size based on the input characteristic. Furthermore, our method universally controls the certified radius decline from the ISS sample size reduction. The empirical results on CIFAR-10 and ImageNet show that ISS can speed up the certification by more than three times at a limited cost of 0. 05 certified radius. Meanwhile, ISS surpasses IAS on the average certified radius across the extensive hyperparameter settings. Specifically, ISS achieves ACR=0. 958 on ImageNet in 250 minutes, compared to ACR=0. 917 by IAS under the same condition. We release our code in https: //github. com/roy-ch/Input-Specific-Certification.

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AAAI Conference 2018 Conference Paper

Action Recognition With Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion

  • Weiyao Lin
  • Chongyang Zhang
  • Ke Lu
  • Bin Sheng
  • Jianxin Wu
  • Bingbing Ni
  • Xin Liu
  • Hongkai Xiong

Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deepbased framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams. We first introduce a coarse-to-fine network which extracts shared deep features at different action class granularities and progressively integrates them to obtain a more accurate feature representation for input actions. We further introduce an asynchronous fusion network. It fuses information from different streams by asynchronously integrating stream-wise features at different time points, hence better leveraging the complementary information in different streams. Experimental results on action recognition benchmarks demonstrate that our approach achieves the state-of-the-art performance.

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