Author name cluster

Bing Li

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74 papers

2 author rows

AAAI Conference 2026 Conference Paper

Ev-iCRF: Self-supervised Event-guided iCRF Estimation for HDR Image Reconstruction

Xucheng Guo
Bing Li
Lin Wang
Yiran Shen

In this paper, we present Ev-iCRF, a novel self-supervised pipeline for high dynamic range (HDR) image reconstruction from a single-exposure low dynamic range (LDR) image, guided by asynchronous event streams generated by a bio-inspired event camera. The highlight of Ev-iCRF lies in its formulation of the inverse camera response function (iCRF) based on Event-LDR Correspondence. By leveraging the HDR properties of event data, the method enables direct iCRF estimation, offering a new perspective for event-guided HDR imaging. The pipeline is trained in a self-supervised manner using formulation-driven iCRF estimation loss and refinement loss, without the need for synchronized HDR supervision. Ev-iCRF adopts a two-stage coarse-to-fine reconstruction pipeline, allowing effective fusion of features from both LDR image and event data. The event information is used to optimize the iCRF, enabling accurate HDR reconstruction from LDR inputs. We evaluate Ev-iCRF on real-world datasets, and results show that it outperforms state-of-the-art methods in HDR reconstruction accuracy. Moreover, the reconstructed images demonstrate improved texture fidelity and structural detail.

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AAAI Conference 2026 Conference Paper

Exploiting Geometric Structures for Modeling Multi-Agent Behaviors: A New Thinking

Bohao Qu
Xiaofeng Cao
Bing Li
Menglin Zhang
Tuan-Anh Vu
Di Lin
Qing Guo

In this paper, we rethink model agent behaviors from a geometric structure perspective in multi-agent reinforcement learning. Modeling agent behaviors is essential for understanding how agents interact and facilitating effective decisions. The key lies in capturing the dependencies and sequential relationships among agent decisions. Since each decision influences the subsequent choices, this forms a hierarchical and nested tree-like structure of interdependencies. While modeling tree-like data in Euclidean spaces could cause distortion, which results in a loss of agent decision structure information. Motivated by this, we reconsider model agent behaviors in hyperbolic space and propose the Hyperbolic Multi-Agent Representations (HMAR) method, which projects the agent behaviors into a Poincaré ball and leverages hyperbolic neural networks to learn agent policy representations. Additionally, we designed a contrastive loss function to train this network, minimizing the distance in feature space between different representations of the same agent while maximizing the distance between representations of distinct agents. Experimental results provide empirical evidence for the effectiveness of the HMAR method in cooperative and competitive environments, demonstrating the potential of hyperbolic agent representations for effective decision-making in multi-agent environments.

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AAAI Conference 2026 Conference Paper

Federated Context-Aware Personalized Recommendation

Zhihao Wang
Xiaoying Liao
Wenke Huang
Bingqian Liu
Tian Chen
Jian Wang
Bing Li

Federated recommender system is emerging as a new paradigm for providing personalized services while preserving user data privacy. Most existing personalized federated recommender systems predict the user's next item by discretely training user and item embeddings. However, this training approach overlooks the user's behavioral patterns, suffers from low interpretability, and requires a substantial amount of data and meticulous fine-tuning to achieve stable and accurate embeddings. To address these limitations, we propose Federated Context-Aware Personalized Recommendation (FedCAR), a novel framework that leverages users’ recent interactions as behavioral context to guide prediction. Instead of static user embeddings, FedCAR dynamically constructs context representations by aggregating and weighting recently interacted item embeddings. Additionally, we incorporate a contrastive learning strategy that enables the model to capture shared behavioral structures across clients while maintaining personalized preferences, enhancing both generalization and robustness in heterogeneous settings. Experiments on 5 benchmark datasets show that FedCAR consistently outperforms state-of-the-art methods and provides interpretable recommendations by explicitly modeling context dependencies.

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AAAI Conference 2026 Conference Paper

Light but Sharp: SlimSTAD for Real-Time Action Detection from Sensor Data

Wei Cui
Lukai Fan
Zhenghua Chen
Min Wu
Shili Xiang
Haixia Wang
Bing Li

Sensory Temporal Action Detection (STAD) aims to localize and classify human actions within long, untrimmed sequences captured by non-visual sensors such as WiFi or inertial measurement units (IMUs). Unlike video-based TAD, STAD poses unique challenges due to the low-dimensional, noisy, and heterogeneous nature of sensory data, as well as the real-time and resource constraints on edge devices. While recent STAD models have improved detection performance, their high computational cost hampers practical deployment. In this paper, we propose SlimSTAD, a simple yet effective framework that achieves both high accuracy and low latency for STAD. SlimSTAD features a novel Decoupled Channel Modeling (DCM) encoder, which preserves modality-specific temporal features and enables efficient inter-channel aggregation via lightweight graph attention. An anchor-free cascade predictor then refines action boundaries and class predictions in a two-stage design without dense proposals. Experiments on two real-world datasets demonstrate that SlimSTAD outperforms strong video-derived and sensory baselines by an average of 2.1 mAP, while significantly reducing GFLOPs, parameters, and latency, validating its effectiveness for real-world, edge-aware STAD deployment.

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AAAI Conference 2026 Short Paper

Misclassification-Aware Robust Learning from Multiple Human Labelers (Student Abstract)

Zuoyuehe Wang
Chicheng Ma
Pengpeng Chen
Lei Chai
Yongqiang Yang
Zhijun Chen
Jingzheng Li
Bing Li

Adversarial training is an effective technique for enhancing the robustness of deep neural networks (DNNs). Prior research shows that misclassified examples influence final adversarial robustness much more than correctly classified examples. Ignoring this difference during training can hurt model performance. In crowdsourcing, varying annotator expertise causes noisy, inconsistent labels. As a result, it is hard to distinguish misclassified and correctly classified examples using only provided annotations. Thus, how to use the reliability and discrepancy between these example types to improve robustness within adversarial learning remains a critical but underexplored issue. In this work, we first explore how misclassified and correctly classified examples affect learning from crowds (LFC) in adversarial environments. Then, we formulate the problem of misclassification-aware robust learning from multiple human labelers as a bilevel min-max problem. After that, we introduce MALC, a new approach to make classifiers more robust to adversarial examples via iterative adversarial example generation and parameter estimation. We conduct an extensive evaluation of the proposed MALC, showing that MALC can outperform the state-of-the-art LFC methods in both white-box and black-box settings.

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AAAI Conference 2026 Conference Paper

MMhops-R1: Multimodal Multi-hop Reasoning

Tao Zhang
Ziqi Zhang
Zongyang Ma
Yuxin Chen
Bing Li
Chunfeng Yuan
Guangting Wang
Fengyun Rao

The ability to perform multi-modal multi-hop reasoning by iteratively integrating information across various modalities and external knowledge is critical for addressing complex real-world challenges. However, existing Multi-modal Large Language Models (MLLMs) are predominantly limited to single-step reasoning, as existing benchmarks lack the complexity needed to evaluate and drive multi-hop abilities. To bridge this gap, we introduce MMhops, a novel, large-scale benchmark designed to systematically evaluate and foster multi-modal multi-hop reasoning. MMhops dataset comprises two challenging task formats, Bridging and Comparison, which necessitate that models dynamically construct complex reasoning chains by integrating external knowledge. To tackle the challenges posed by MMhops, we propose MMhops-R1, a novel multi-modal Retrieval-Augmented Generation (mRAG) framework for dynamic reasoning. Our framework utilizes reinforcement learning to optimize the model for autonomously planning reasoning paths, formulating targeted queries, and synthesizing multi-level information. Comprehensive experiments demonstrate that MMhops-R1 significantly outperforms strong baselines on MMhops, highlighting that dynamic planning and multi-modal knowledge integration are crucial for complex reasoning. Moreover, MMhops-R1 demonstrates strong generalization to tasks requiring fixed-hop reasoning, underscoring the robustness of our dynamic planning approach.

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EAAI Journal 2026 Journal Article

Multi-view knowledge graph recommendation with dynamic transformer modeling

Mengting Zhang
Man Li
Wenming Cao
Mingming Yang
Bing Li
Guangzhen Zhu
Lu Cheng

Recommender systems are essential for alleviating information overload but continue to face challenges such as data sparsity, cold-start issues, and noisy knowledge graph structures. To address these problems, we propose a novel recommendation framework that integrates a Dynamic Tanh Transformer (DyT-Transformer), a multi-view knowledge graph propagation strategy, and an inter-layer attention mechanism. The DyT-Transformer enhances user-item representations by dynamically weighting key features without normalization layers, improving stability and efficiency. Multi-view propagation is employed to generate diverse graph views through randomized perturbations, which mitigates noise and improves robustness. Inter-layer attention adaptively aggregates embedding across propagation depths to capture hierarchical semantics. Extensive experiments on five real-world datasets, including Book-Crossing, MovieLens-1M, Amazon-Book, Yelp2018, and Alibaba, demonstrate that the proposed model consistently outperforms state-of-the-art baselines in both click-through rate prediction and Top-K recommendation tasks. These results confirm the effectiveness and scalability of combining DyT-Transformer with multi-view knowledge graph learning for robust recommendation. We have made the source code of our proposed model available at https: //github. com/tttterter/MVLDT.

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JMLR Journal 2026 Journal Article

Nonlinear function-on-function regression by RKHS

Peijun Sang
Bing Li

We propose a nonlinear function-on-function regression model where both the covariate and the response are random functions. The nonlinear regression is carried out in two steps: we first construct Hilbert spaces to accommodate the functional covariate and the functional response, and then build a second-layer Hilbert space for the covariate to capture nonlinearity. The second-layer space is assumed to be a reproducing kernel Hilbert space, which is generated by a positive definite kernel determined by the inner product of the first-layer Hilbert space for $X$--this structure is known as the nested Hilbert spaces. We develop estimation procedures to implement the proposed method, which allows the functional data to be observed at different time points for different subjects. Furthermore, we establish the convergence rate of our estimator as well as the weak convergence of the predicted response in the Hilbert space. Numerical studies including both simulations and a data application are conducted to investigate the performance of our estimator in finite sample. [abs] [ pdf ][ bib ] &copy JMLR 2026. ( edit, beta )