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Siming Zheng

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7 papers

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EAAI Journal 2026 Journal Article

A lightweight and real-time surgical action detection framework using multi-contextual and decoupled representations

Siming Zheng
A.S.M. Sharifuzzaman Sagar
Yu Chen
Jun Hoong Chan
Zehao Yu
Shi Ying
Jianfeng Lu

Accurate detection of surgical actions in minimally invasive procedures is a critical step toward developing intelligent operative assistance systems. In this work, we propose Surgical You Only Look Once detector (Surg-YOLO), an efficient and high-precision surgical action detection framework built upon the YOLO version 11 (YOLOv11) architecture, specifically optimized for the spatio-temporal complexities of surgical environments. Surg-YOLO integrates three key architectural innovations: the Enhanced Spatial Pyramid Pooling-Fast (ESPPF) module for capturing rich multi-scale spatial features; the Spatio-Temporal Multi-scale Context Aggregation Module (ST-MCAM), which enhances temporal reasoning and contextual awareness across frames; and the Decoupled Dual-Branch Prediction Head (DDPH) for independently refining classification and localization tasks. Extensive experiments on a large-scale surgical action dataset demonstrate that Surg-YOLO significantly outperforms existing baseline models, achieving superior detection accuracy across multiple evaluation thresholds. Qualitative visualizations further validate the model’s ability to localize subtle and concurrent surgical actions with high precision. These results highlight Surg-YOLO’s potential as a reliable solution for real-time surgical action detection.

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AAAI Conference 2026 Conference Paper

Breaking Measurement Barriers: From Compressed Sensing to Deep Reconstruction

Gang Qu
Ping Wang
Siming Zheng
Xin Yuan

Deep learning methods have achieved remarkable success in image compressed sensing (CS) task, namely reconstructing a high-fidelity image from its compressed measurement. However, existing methods are deficient in incoherent compressed measurement at sensing phase and implicit measurement representations at reconstruction phase, limiting the overall performance. In this work, we answer two questions: (i) how to improve the measurement incoherence for decreasing the ill-posedness; (ii) how to learn informative representations from measurements. To this end, we propose a novel asymmetric Kronecker CS (AKCS) model and theoretically present its better incoherence than previous Kronecker CS with minimal increase of complexity. Moreover, apart from the explicit measurement representations in gradient descent projection in unfolding networks, we further propose a measurement-aware cross attention (MACA) mechanism to learn implicit measurement representations. We integrate AKCS and MACA into a widely-used unfolding architecture to get a measurement-enhanced unfolding network (MEUNet). Extensive experiments demonstrate that the proposed MEUNet achieves state-of-the-art (SOTA) performance in reconstruction accuracy with high efficiency.

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JMLR Journal 2026 Journal Article

Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence

Siming Zheng
Guohao Shen
Yuanyuan Lin
Jian Huang

We consider the problem of density-ratio estimation using Bregman Divergence with Deep ReLU feedforward neural networks (BDD). We establish non-asymptotic error bounds for BDD density-ratio estimators, which are minimax optimal up to a logarithmic factor when the data distribution has finite support. As an application of our theoretical findings, we propose an estimator for the KL-divergence that is asymptotically normal, leveraging our convergence results for the deep density-ratio estimator and a data-splitting method. We also extend our results to cases with unbounded support and unbounded density ratios. Furthermore, we show that the BDD density-ratio estimator can mitigate the curse of dimensionality when data distributions are supported on an approximately low-dimensional manifold. Our results are applied to investigate the convergence properties of the telescoping density-ratio estimator proposed by Rhodes (2020). We provide sufficient conditions under which it achieves a lower error bound than a single-ratio estimator. Moreover, we conduct simulation studies to validate our main theoretical results and assess the performance of the BDD density-ratio estimator. [abs] [ pdf ][ bib ] &copy JMLR 2026. ( edit, beta )

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AAAI Conference 2026 Conference Paper

Realism Control One-step Diffusion for Real-world Image Super Resolution

Zongliang Wu
Siming Zheng
Peng-tao Jiang
Xin Yuan

Pre-trained diffusion models have shown great potential in real-world image super-resolution (Real-ISR) tasks by enabling high-resolution reconstructions. While one-step diffusion (OSD) methods significantly improve efficiency compared to traditional multi-step approaches, they still have limitations in balancing fidelity and realism across diverse scenarios. Since the OSDs for SR are usually trained or distilled by a single timestep, they lack flexible control mechanisms to adaptively prioritize these competing objectives, which are inherently manageable in multi-step methods through adjusting sampling steps. To address this challenge, we propose a Realism Controlled One-step Diffusion (RCOD) framework for Real-ISR. RCOD provides a latent domain grouping strategy that enables explicit control over fidelity-realism trade-offs during the noise prediction phase with minimal training paradigm modifications and original training data. A degradation-aware sampling strategy is also introduced to align distillation regularization with the grouping strategy and enhance the controlling of trade-offs. Moreover, a visual prompt injection module is used to replace conventional text prompts with degradation-aware visual tokens, enhancing both restoration accuracy and semantic consistency. Our method achieves superior fidelity and perceptual quality while maintaining computational efficiency. Extensive experiments demonstrate that RCOD outperforms state-of-the-art OSD methods in both quantitative metrics and visual qualities, with flexible realism control capabilities in the inference stage.

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NeurIPS Conference 2025 Conference Paper

Photography Perspective Composition: Towards Aesthetic Perspective Recommendation

Lujian Yao
Siming Zheng
Xinbin Yuan
Zhuoxuan Cai
Pu Wu
Jinwei Chen
Bo Li
Peng-tao Jiang

Traditional photography composition approaches are dominated by 2D cropping-based methods. However, these methods fall short when scenes contain poorly arranged subjects. Professional photographers often employ perspective adjustment as a form of 3D recomposition, modifying the projected 2D relationships between subjects while maintaining their actual spatial positions to achieve better compositional balance. Inspired by this artistic practice, we propose photography perspective composition (PPC), extending beyond traditional cropping-based methods. However, implementing the PPC faces significant challenges: the scarcity of perspective transformation datasets and undefined assessment criteria for perspective quality. To address these challenges, we present three key contributions: (1) An automated framework for building PPC datasets through expert photographs. (2) A video generation approach that demonstrates the transformation process from less favorable to aesthetically enhanced perspectives. (3) A perspective quality assessment (PQA) model constructed based on human performance. Our approach is concise and requires no additional prompt instructions or camera trajectories, helping and guiding ordinary users to enhance their composition skills.

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EAAI Journal 2024 Journal Article

A multi-view consistency framework with semi-supervised domain adaptation

Yuting Hong
Li Dong
Xiaojie Qiu
Hui Xiao
Baochen Yao
Siming Zheng
Chengbin Peng

Semi-Supervised Domain Adaptation (SSDA) leverages knowledge from a fully labeled source domain to classify data in a partially labeled target domain. Due to the limited number of labeled samples in the target domain, there can be intrinsic similarity of classes in the feature space, which may result in biased predictions, even when the model is trained on a balanced dataset. To overcome this limitation, we introduce a multi-view consistency framework, which includes two views for training strongly augmented data. One is a debiasing strategy for correcting class-wise prediction probabilities according to the prediction performance of the model. The other involves leveraging pseudo-negative labels derived from the model predictions. Furthermore, we introduce a cross-domain affinity learning aimed at aligning features of the same class across different domains, thereby enhancing overall performance. Experimental results demonstrate that our method outperforms the competing methods on two standard domain adaptation datasets, DomainNet and Office–Home. Combining unsupervised domain adaptation and semi-supervised learning offers indispensable contributions to the industrial sector by enhancing model adaptability, reducing annotation costs, and improving performance.

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AAAI Conference 2023 Conference Paper

Deep Equilibrium Models for Snapshot Compressive Imaging

Yaping Zhao
Siming Zheng
Xin Yuan

The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data has led to an inverse problem, which consists of recovering the HD signal from the compressed and noisy measurement. While reconstruction algorithms grow fast to solve it with the recent advances of deep learning, the fundamental issue of accurate and stable recovery remains. To this end, we propose deep equilibrium models (DEQ) for video SCI, fusing data-driven regularization and stable convergence in a theoretically sound manner. Each equilibrium model implicitly learns a nonexpansive operator and analytically computes the fixed point, thus enabling unlimited iterative steps and infinite network depth with only a constant memory requirement in training and testing. Specifically, we demonstrate how DEQ can be applied to two existing models for video SCI reconstruction: recurrent neural networks (RNN) and Plug-and-Play (PnP) algorithms. On a variety of datasets and real data, both quantitative and qualitative evaluations of our results demonstrate the effectiveness and stability of our proposed method. The code and models are available at: https://github.com/IndigoPurple/DEQSCI.

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