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Li Cheng

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20 papers

2 author rows

EAAI Journal 2026 Journal Article

A robust zero-shot framework based on adaptive illumination perception and trichromatic calibration for extreme low-light image enhancement

Xuan Li
Zhaoming Feng
Weiwei Chen
Guomin Zhang
Yifan Ding
Li Cheng

Zero-shot low-light image enhancement methods eliminate the dependency on annotated data while demonstrating strong generalization capabilities. However, existing zero-shot methods fail to effectively handle extreme low-light conditions, resulting in insufficient brightness, over-exposure and color distortion. To address these issues, we propose a robust zero-shot framework based on adaptive illumination perception and trichromatic calibration for extreme low-light image enhancement, termed Zero-IPTC. In the proposed framework, an adaptive illumination perception network is designed to estimate enhancement curves by using asymmetric skip connections and hybrid attention modules. These structures endow the network with stronger representational power to flexibly recover details even under extreme illumination variations. Furthermore, we propose a trichromatic calibration mechanism to optimize the enhancement curves by modeling the inter-channel contrast relationships. The mechanism can significantly improve the color fidelity. Extensive experiments under diverse illumination conditions demonstrate that the proposed framework achieves state-of-the-art performance, achieving an average Naturalness Image Quality Evaluator (NIQE) score of 3. 04 and Lightness-Order-Error (LOE) score of 391. 2 across four benchmark datasets. The framework also showcases its adaptability in security surveillance and autonomous driving scenarios.

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AAAI Conference 2026 Conference Paper

Online Multi-Relational Clustering with Dominant View Mining

Zhengzhong Zhu
Pei Zhou
Dongsheng Wang
Li Cheng
Jiangping Zhu

Multi-relational graph clustering aims to uncover complex node interactions by leveraging multiple relational views, yet existing methods often suffer from two key limitations: they assume equal importance across views and decouple representation learning from clustering, both of which hinder overall performance. To address these issues, we propose OMC-DVM, a novel end-to-end Online Multi-Relational Graph Clustering With Dominant View Mining framework. OMC-DVM introduces two core innovations: (1) A unsupervised dominant view mining module that dynamically identifies the dominant view using Maximum Mean Discrepancy (MMD) and adaptively aligns other views to it, mitigating view imbalance. (2) An online,multi-relational clustering process that unifies representation learning and clustering into a single stage. By performing clustering-level contrastive learning, OMC-DVM directly generates cluster assignments in an end-to-end manner. Extensive experiments on both real-world and synthetic benchmark datasets demonstrate that OMC-DVM not only achieves state-of-the-art clustering performance but also effectively alleviates the view imbalance problem in multi-relational graphs.

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AAAI Conference 2026 Conference Paper

VPHO: Joint Visual-Physical Cue Learning and Aggregation for Hand-Object Pose Estimation

Jun Zhou
Chi Xu
Kaifeng Tang
Yuting Ge
Tingrui Guo
Li Cheng

Estimating the 3D poses of hands and objects from a single RGB image is a fundamental yet challenging problem, with broad applications in augmented reality and human-computer interaction. Existing methods largely rely on visual cues alone, often producing results that violate physical constraints such as interpenetration or non-contact. Recent efforts to incorporate physics reasoning typically depend on post-optimization or non-differentiable physics engines, which compromise visual consistency and end-to-end trainability. To overcome these limitations, we propose a novel framework that jointly integrates visual and physical cues for hand-object pose estimation. This integration is achieved through two key ideas: 1) joint visual-physical cue learning: The model is trained to extract 2D visual cues and 3D physical cues, thereby enabling more comprehensive representation learning for hand-object interactions; 2) candidate pose aggregation: A novel refinement process that aggregates multiple diffusion-generated candidate poses by leveraging both visual and physical predictions, yielding a final estimate that is visually consistent and physically plausible. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches in both pose accuracy and physical plausibility.

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NeurIPS Conference 2025 Conference Paper

Act to See, See to Act: Diffusion-Driven Perception-Action Interplay for Adaptive Policies

Jing Wang
Weiting Peng
Jing Tang
Zeyu Gong
Xihua Wang
Bo Tao
Li Cheng

Existing imitation learning methods decouple perception and action, which overlooks the causal reciprocity between sensory representations and action execution that humans naturally leverage for adaptive behaviors. To bridge this gap, we introduce Action-Guided Diffusion Policy (DP-AG), a unified representation learning that explicitly models a dynamic interplay between perception and action through probabilistic latent dynamics. DP-AG encodes latent observations into a Gaussian posterior via variational inference and evolves them using an action-guided SDE, where the Vector–Jacobian Product (VJP) of the diffusion policy's noise predictions serves as a structured stochastic force driving latent updates. To promote bidirectional learning between perception and action, we introduce a cycle-consistent contrastive loss that organizes the gradient flow of the noise predictor into a coherent perception–action loop, enforcing mutually consistent transitions in both latent updates and action refinements. Theoretically, we derive a variational lower bound for the action-guided SDE, and prove that the contrastive objective enhances continuity in both latent and action trajectories. Empirically, DP-AG significantly outperforms state-of-the-art methods across simulation benchmarks and real-world UR5 manipulation tasks. As a result, our DP-AG offers a promising step toward bridging biological adaptability and artificial policy learning. Code is available on our project website: https: //jingwang18. github. io/dp-ag. github. io/.

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AAAI Conference 2025 Conference Paper

HiPoser: 3D Human Pose Estimation with Hierarchical Shared Learning at Parts-Level Using Inertial Measurement Units

Guorui Liao
Chunyuan Zheng
Li Cheng
Haoyu Xie
Shanshan Huang
Jun Liao
Haoxuan Li
Li Liu

This paper considers the challenging problem of 3D Human Pose Estimation (HPE) from a sparse set of Inertial Measurement Units (IMUs). Existing efforts typically reconstruct a pose sequence by either directly tackling whole-body motions or focusing on distinctive spatio-temporal features of local body parts. Unfortunately, these methods ignore existing interdependent motor synergies amongst body parts, which may lead to pose estimation with ambiguous local parts. This observation motivates us to propose a hierarchical learning-based approach, HiPoser, which utilizes a hierarchical shared structure using Mamba blocks as the backbone to focus on the following estimation tasks, involving: 1) torso pose, 2) lower limbs pose, 3) upper limbs pose, and finally 4) global translation. These tasks selectively incorporate body motion states and are to be carried out sequentially in reconstructing part-based poses, which are amalgamated to estimate the final full-body pose with the global translation that satisfies inter-part consistencies. Our hierarchical structure allows HiPoser the flexibility in prioritizing different aspects of pose estimation, to emphasize more on detail or stability. Empirical evaluations over three benchmark datasets demonstrate the superiority of HiPoser over existing state-of-the-art models, suggesting that analyzing the synergistic movement of body parts is indeed important for advancing IMU-based 3D HPE.

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IROS Conference 2025 Conference Paper

MotionScript: Natural Language Descriptions for Expressive 3D Human Motions

Payam Jome Yazdian
Rachel Lagasse
Hamid Mohammadi
Eric Liu
Li Cheng
Angelica Lim

We introduce MotionScript, a novel framework for generating highly detailed, natural language descriptions of 3D human motions. Unlike existing motion datasets that rely on broad action labels or generic captions, MotionScript provides fine-grained, structured descriptions that capture the full complexity of human movement—including expressive actions (e. g. , emotions, stylistic walking) and interactions beyond standard motion capture datasets. MotionScript serves as both a descriptive tool and a training resource for text-to-motion models, enabling the synthesis of highly realistic and diverse human motions from text. By augmenting motion datasets with MotionScript captions, we demonstrate significant improvements in out-of-distribution motion generation, allowing large language models (LLMs) to generate motions that extend beyond existing data. Additionally, MotionScript opens new applications in animation, virtual human simulation, and robotics, providing an interpretable bridge between intuitive descriptions and motion synthesis. To the best of our knowledge, this is the first attempt to systematically translate 3D motion into structured natural language without requiring training data. Code, dataset, and examples are available at https://pjyazdian.github.io/MotionScript

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NeurIPS Conference 2024 Conference Paper

Unleashing Multispectral Video's Potential in Semantic Segmentation: A Semi-supervised Viewpoint and New UAV-View Benchmark

Wei Ji
Jingjing Li
Wenbo Li
Yilin Shen
Li Cheng
Hongxia Jin

Thanks to the rapid progress in RGB & thermal imaging, also known as multispectral imaging, the task of multispectral video semantic segmentation, or MVSS in short, has recently drawn significant attentions. Noticeably, it offers new opportunities in improving segmentation performance under unfavorable visual conditions such as poor light or overexposure. Unfortunately, there are currently very few datasets available, including for example MVSeg dataset that focuses purely toward eye-level view; and it features the sparse annotation nature due to the intensive demands of labeling process. To address these key challenges of the MVSS task, this paper presents two major contributions: the introduction of MVUAV, a new MVSS benchmark dataset, and the development of a dedicated semi-supervised MVSS baseline - SemiMV. Our MVUAV dataset is captured via Unmanned Aerial Vehicles (UAV), which offers a unique oblique bird’s-eye view complementary to the existing MVSS datasets; it also encompasses a broad range of day/night lighting conditions and over 30 semantic categories. In the meantime, to better leverage the sparse annotations and extra unlabeled RGB-Thermal videos, a semi-supervised learning baseline, SemiMV, is proposed to enforce consistency regularization through a dedicated Cross-collaborative Consistency Learning (C3L) module and a denoised temporal aggregation strategy. Comprehensive empirical evaluations on both MVSeg and MVUAV benchmark datasets have showcased the efficacy of our SemiMV baseline.

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NeurIPS Conference 2023 Conference Paper

Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild

Yanhui Guo
Xinxin Zuo
Peng Dai
Juwei Lu
Xiaolin Wu
Li Cheng
Youliang Yan
Songcen Xu

This paper presents Decorate3D, a versatile and user-friendly method for the creation and editing of 3D objects using images. Decorate3D models a real-world object of interest by neural radiance field (NeRF) and decomposes the NeRF representation into an explicit mesh representation, a view-dependent texture, and a diffuse UV texture. Subsequently, users can either manually edit the UV or provide a prompt for the automatic generation of a new 3D-consistent texture. To achieve high-quality 3D texture generation, we propose a structure-aware score distillation sampling method to optimize a neural UV texture based on user-defined text and empower an image diffusion model with 3D-consistent generation capability. Furthermore, we introduce a few-view resampling training method and utilize a super-resolution model to obtain refined high-resolution UV textures (2048$\times$2048) for 3D texturing. Extensive experiments collectively validate the superior performance of Decorate3D in retexturing real-world 3D objects. Project page: https: //decorate3d. github. io/Decorate3D/.

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NeurIPS Conference 2023 Conference Paper

DVSOD: RGB-D Video Salient Object Detection

Jingjing Li
Wei Ji
Size Wang
Wenbo Li
Li Cheng

Salient object detection (SOD) aims to identify standout elements in a scene, with recent advancements primarily focused on integrating depth data (RGB-D) or temporal data from videos to enhance SOD in complex scenes. However, the unison of two types of crucial information remains largely underexplored due to data constraints. To bridge this gap, we in this work introduce the DViSal dataset, fueling further research in the emerging field of RGB-D video salient object detection (DVSOD). Our dataset features 237 diverse RGB-D videos alongside comprehensive annotations, including object and instance-level markings, as well as bounding boxes and scribbles. These resources enable a broad scope for potential research directions. We also conduct benchmarking experiments using various SOD models, affirming the efficacy of multimodal video input for salient object detection. Lastly, we highlight some intriguing findings and promising future research avenues. To foster growth in this field, our dataset and benchmark results are publicly accessible at: https: //dvsod. github. io/.

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JBHI Journal 2022 Journal Article

DPProm: A Two-Layer Predictor for Identifying Promoters and Their Types on Phage Genome Using Deep Learning

Chen Wang
Junyin Zhang
Li Cheng
Jiawei Wu
Minfeng Xiao
Junfeng Xia
Yannan Bin

With the number of phage genomes increasing, it is urgent to develop new bioinformatics methods for phage genome annotation. Promoter, a DNA region, is important for gene transcriptional regulation. In the era of post-genomics, the availability of data makes it possible to establish computational models for promoter identification with robustness. In this work, we introduce DPProm, a two-layer model composed of DPProm-1L and DPProm-2L, to predict promoters and their types for phages. On the first layer, as a dual-channel deep neural network ensemble method fusing multi-view features (sequence feature and handcrafted feature), the model DPProm-1L is proposed to identify whether a DNA sequence is a promoter or non-promoter. The sequence feature is extracted with convolutional neural network (CNN). And the handcrafted feature is the combination of free energy, GC content, cumulative skew, and Z curve features. On the second layer, DPProm-2L based on CNN is trained to predict the promoters' types (host or phage). For the realization of prediction on the whole genomes, the model DPProm, combines with a novel sequence data processing workflow, which contains sliding window and merging sequences modules. Experimental results show that DPProm outperforms the state-of-the-art methods, and decreases the false positive rate effectively on whole genome prediction. Furthermore, we provide a user-friendly web at http://bioinfo.ahu.edu.cn/DPProm.We expect that DPProm can serve as a useful tool for identification of promoters and their types.

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IJCAI Conference 2022 Conference Paper

Music-to-Dance Generation with Optimal Transport

Shuang Wu
Shijian Lu
Li Cheng

Dance choreography for a piece of music is a challenging task, having to be creative in presenting distinctive stylistic dance elements while taking into account the musical theme and rhythm. It has been tackled by different approaches such as similarity retrieval, sequence-to-sequence modeling and generative adversarial networks, but their generated dance sequences are often short of motion realism, diversity and music consistency. In this paper, we propose a Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning to generate 3D dance choreographies from music. We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music. This gives a well defined and non-divergent training objective that mitigates the limitation of standard GAN training which is frequently plagued with instability and divergent generator loss issues. Extensive experiments demonstrate that our MDOT-Net can synthesize realistic and diverse dances which achieve an organic unity with the input music, reflecting the shared intentionality and matching the rhythmic articulation. Sample results are found at https: //www. youtube. com/watch? v=dErfBkrlUO8.

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NeurIPS Conference 2021 Conference Paper

Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection

Jingjing Li
Wei Ji
Qi Bi
Cheng Yan
Miao Zhang
Yongri Piao
Huchuan Lu
Li Cheng

Training saliency detection models with weak supervisions, e. g. , image-level tags or captions, is appealing as it removes the costly demand of per-pixel annotations. Despite the rapid progress of RGB-D saliency detection in fully-supervised setting, it however remains an unexplored territory when only weak supervision signals are available. This paper is set to tackle the problem of weakly-supervised RGB-D salient object detection. The key insight in this effort is the idea of maintaining per-pixel pseudo-labels with iterative refinements by reconciling the multimodal input signals in our joint semantic mining (JSM). Considering the large variations in the raw depth map and the lack of explicit pixel-level supervisions, we propose spatial semantic modeling (SSM) to capture saliency-specific depth cues from the raw depth and produce depth-refined pseudo-labels. Moreover, tags and captions are incorporated via a fill-in-the-blank training in our textual semantic modeling (TSM) to estimate the confidences of competing pseudo-labels. At test time, our model involves only a light-weight sub-network of the training pipeline, i. e. , it requires only an RGB image as input, thus allowing efficient inference. Extensive evaluations demonstrate the effectiveness of our approach under the weakly-supervised setting. Importantly, our method could also be adapted to work in both fully-supervised and unsupervised paradigms. In each of these scenarios, superior performance has been attained by our approach with comparing to the state-of-the-art dedicated methods. As a by-product, a CapS dataset is constructed by augmenting existing benchmark training set with additional image tags and captions.

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AAAI Conference 2021 Conference Paper

Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection

Li Cheng
Yijie Wang
Xinwang Liu

Multi-view outlier detection recently attracted rapidly growing attention with the development of multi-view learning. Although promising performance demonstrated, we observe that identifying outliers in multi-view data is still a challenging task due to the complicated characteristics of multi-view data. Specifically, an effective multi-view outlier detection method should be able to handle (1) different types of outliers; (2) two or more views; (3) samples without clusters; (4) high dimensional data. Unfortunately, little is known about how these four issues can be handled simultaneously. In this paper, we propose an unsupervised multi-view outlier detection method to address these issues. Our method is based on the proposed novel neighborhood consensus networks termed NC-Nets, which automatically encodes intrinsic information into a comprehensive latent space for each view (for issue (4)) and uniforms the neighborhood structures among different views (for issue (2)). Accordingly, we propose an outlier score measurement which consists of two parts: the withinview reconstruction score and the cross-view neighborhood consensus score. The measurement is designed based on the characteristics of the different outlier types (for issue (1)) and no cluster assumption is needed (for issue (3)). Experimental results show that our method significantly outperforms state-of-the-art methods. On average, our method achieves 11. 2% ∼ 96. 2% improvement in term of AUC and 33. 5% ∼ 352. 7% improvement in term of F1-Score.

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AAAI Conference 2020 Conference Paper

Outlier Detection Ensemble with Embedded Feature Selection

Li Cheng
Yijie Wang
Xinwang Liu
Bin Li

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Speciﬁcally, for each random sub-sampling based learning component, ODEFS uniﬁes feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS.

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AAAI Conference 2018 Conference Paper

Multi-Modal Multi-Task Learning for Automatic Dietary Assessment

Qi Liu
Yue Zhang
Zhenguang Liu
Ye Yuan
Li Cheng
Roger Zimmermann

We investigate the task of automatic dietary assessment: given meal images and descriptions uploaded by real users, our task is to automatically rate the meals and deliver advisory comments for improving users’ diets. To address this practical yet challenging problem, which is multi-modal and multi-task in nature, an end-to-end neural model is proposed. In particular, comprehensive meal representations are obtained from images, descriptions and user information. We further introduce a novel memory network architecture to store meal representations and reason over the meal representations to support predictions. Results on a real-world dataset show that our method outperforms two strong image captioning baselines signiﬁcantly.

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AAAI Conference 2017 Conference Paper

Multi-Kernel Low-Rank Dictionary Pair Learning for Multiple Features Based Image Classification

Xiaoke Zhu
Xiao-Yuan Jing
Fei Wu
Di Wu
Li Cheng
Sen Li
Ruimin Hu

Dictionary learning (DL) is an effective feature learning technique, and has led to interesting results in many classiﬁcation tasks. Recently, by combining DL with multiple kernel learning (which is a crucial and effective technique for combining different feature representation information), a few multi-kernel DL methods have been presented to solve the multiple feature representations based classiﬁcation problem. However, how to improve the representation capability and discriminability of multi-kernel dictionary has not been well studied. In this paper, we propose a novel multi-kernel DL approach, named multi-kernel low-rank dictionary pair learning (MKLDPL). Speciﬁcally, MKLDPL jointly learns a kernel synthesis dictionary and a kernel analysis dictionary by exploiting the class label information. The learned synthesis and analysis dictionaries work together to implement the coding and reconstruction of samples in the kernel space. To enhance the discriminability of the learned multi-kernel dictionaries, MKLDPL imposes the low-rank regularization on the analysis dictionary, which can make samples from the same class have similar representations. We apply MKLDPL for multiple features based image classiﬁcation task. Experimental results demonstrate the effectiveness of the proposed approach.

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AAAI Conference 2016 Conference Paper

Recognizing Complex Activities by a Probabilistic Interval-Based Model

Li Liu
Li Cheng
Ye Liu
Yongpo Jia
David Rosenblum

A key challenge in complex activity recognition is the fact that a complex activity can often be performed in several different ways, with each consisting of its own conﬁguration of atomic actions and their temporal dependencies. This leads us to deﬁne an atomic activity-based probabilistic framework that employs Allen’s interval relations to represent local temporal dependencies. The framework introduces a latent variable from the Chinese Restaurant Process to explicitly characterize these unique internal conﬁgurations of a particular complex activity as a variable number of tables. It can be analytically shown that the resulting interval network satisﬁes the transitivity property, and as a result, all local temporal dependencies can be retained and are globally consistent. Empirical evaluations on benchmark datasets suggest our approach signiﬁcantly outperforms the state-of-the-art methods.

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ICML Conference 2013 Conference Paper

Riemannian Similarity Learning

Li Cheng

We consider a similarity-score based paradigm to address scenarios where either the class labels are only partially revealed during learning, or the training and testing data are drawn from heterogeneous sources. The learning problem is subsequently formulated as optimization over a bilinear form of fixed rank. Our paradigm bears similarity to metric learning, where the major difference lies in its aim of learning a rectangular similarity matrix, instead of a proper metric. We tackle this problem in a Riemannian optimization framework. In particular, we consider its applications in pairwise-based action recognition, and cross-domain image-based object recognition. In both applications, the proposed algorithm produces competitive performance on respective benchmark datasets.

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AAAI Conference 2008 Conference Paper

Prediction and Change Detection in Sequential Data for Interactive Applications

Li Cheng

We consider the problems of sequential prediction and change detection that arise often in interactive applications: A semi-automatic predictor is applied to a time-series and is expected to make proper predictions and request new human input when change points are detected. Motivated by the Transductive Support Vector Machines (Vapnik 1998), we propose an online framework that naturally addresses these problems in a unified manner. Our empirical study with a synthetic dataset and a road tracking dataset demonstrates the efficacy of the proposed approach.

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NeurIPS Conference 2006 Conference Paper

implicit Online Learning with Kernels

Li Cheng
Dale Schuurmans
Shaojun Wang
Terry Caelli
S. V. N. Vishwanathan

We present two new algorithms for online learning in reproducing kernel Hilbert spaces. Our first algorithm, ILK (implicit online learning with kernels), employs a new, implicit update technique that can be applied to a wide variety of convex loss functions. We then introduce a bounded memory version, SILK (sparse ILK), that maintains a compact representation of the predictor without compromising solution quality, even in non-stationary environments. We prove loss bounds and analyze the convergence rate of both. Experimental evidence shows that our proposed algorithms outperform current methods on synthetic and real data.

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