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Bolin Zhang

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4 papers

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EAAI Journal 2026 Journal Article

Flare detection and detail compensation for nighttime flare removal

Yuzhen Niu
Bolin Zhang
Yuezhou Li
Jingyuan Zheng

Images captured at nighttime are often impaired by flares of varying sizes and shapes, which degrade visual perception and hinder image quality. Distinguishing these flares from light sources for effective removal is challenging due to the complex pattern. In addition, the image regions affected by the flare show insufficient texture details, resulting in poor visual perception at nighttime. In this paper, we propose the flare detection and detail compensation for nighttime flare removal. Our method consists of two stages: the flare detection stage and the flare removal with detail compensation stage. Specifically, in the flare detection stage, the flare perception feature is extracted through an encoder–decoder structure composed of Multi-receptive Field Collaborative Perception Blocks (MCPBs), and then a flare mask that can distinguish flares, light sources, and other regions is generated. In the flare removal with detail compensation stage, the flare perception feature obtained in the previous stage and the edge detail information of the input image are first fused. Based on the fused informative feature, another encoder–decoder structure composed of residual blocks and MCPBs is used to achieve flare removal, light source preservation, and detail compensation at the same time. Experiments on the Flare7K dataset confirm that our method outperforms state-of-the-art models in both quantitative and qualitative evaluations, delivering superior image quality and more visually appealing results. To summarize, experimental results suggest the potential for our approach to improve nighttime imaging by removing flare degradation in various applications. Future research on the nighttime flare removal requires further improvement of its generalization capabilities, particularly in scenarios with extreme illumination conditions or severe flare interference.

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AAAI Conference 2026 Conference Paper

ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks

Kaijun Wang
Liqin Lu
Mingyu Liu
Jianuo Jiang
Zeju Li
Bolin Zhang
Wancai Zheng
Xinyi Yu

Language-guided long-horizon mobile manipulation has long been a grand challenge in embodied semantic reasoning, generalizable manipulation, and adaptive locomotion. Three fundamental limitations hinder progress: First, although large language models have shown promise in enhancing spatial reasoning and task planning through learned semantic priors, existing implementations remain confined to tabletop scenarios, failing to address the constrained perception and limited actuation ranges characteristic of mobile platforms. Second, current manipulation strategies exhibit insufficient generalization when confronted with the diverse object configurations encountered in open-world environments. Third, while crucial for practical deployment, the dual requirement of maintaining high platform maneuverability alongside precise end-effector control in unstructured settings remains understudied in the literature. In this work, we present ODYSSEY, a unified mobile manipulation framework for agile quadruped robots equipped with manipulators, which seamlessly integrates high-level task planning with low-level whole-body control. To address the challenge of egocentric perception in language-conditioned tasks, we introduce a hierarchical planner powered by a vision-language model, enabling long-horizon instruction decomposition and precise action execution. At the control level, our novel whole-body policy achieves robust coordination of locomotion and manipulation across challenging terrains. We further present the first comprehensive benchmark for long-horizon mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through successful sim-to-real transfer, we demonstrate the system’s generalization and robustness in real-world deployments, underscoring the practicality of legged manipulators in unstructured environments. Our work advances the feasibility of generalized robotic assistants capable of complex, dynamic tasks.

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JAIR Journal 2025 Journal Article

A Survey on Data Selection for LLM Instruction Tuning

Bolin Zhang
Jiahao Wang
Qianlong Du
Jiajun Zhang
Zhiying Tu
Dianhui Chu

Instruction tuning is a vital step of training large language models (LLMs), so how to enhance the effect of instruction tuning has received increased attention. Existing works indicate that the quality of the dataset is more crucial than the quantity during instruction tuning of LLMs. Therefore, recently a lot of studies focus on exploring the methods of selecting high-quality subset from instruction datasets, aiming to reduce training costs and enhance the instruction-following capabilities of LLMs. This paper presents a comprehensive survey on data selection for LLM instruction tuning. Firstly, we introduce the wildly used instruction datasets. Then, we propose a new taxonomy of the data selection methods and provide a detailed introduction of recent advances, and the evaluation strategies and results of data selection methods are also elaborated in detail. Finally, we emphasize the open challenges and present new frontiers of this task.

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TIST Journal 2024 Journal Article

A Federated Social Recommendation Approach with Enhanced Hypergraph Neural Network

Hongliang Sun
Zhiying Tu
Dianbo Sui
Bolin Zhang
Xiaofei Xu

In recent years, the development of online social network platforms has led to increased research efforts in social recommendation systems. Unlike traditional recommendation systems, social recommendation systems utilize both user-item interactions and user-user social relations to recommend relevant items, taking into account social homophily and social influence. Graph neural network (GNN)-based social recommendation methods have been proposed to model these item interactions and social relations effectively. However, existing GNN-based methods rely on centralized training, which raises privacy concerns and faces challenges in data collection due to regulations and privacy restrictions. Federated learning has emerged as a privacy-preserving alternative. Combining federated learning with GNN-based methods for social recommendation can leverage their respective advantages, but it also introduces new challenges: (1) existing federated recommendation systems often lack the capability to process heterogeneous data, such as user-item interactions and social relations; (2) due to the sparsity of data distributed across different clients, capturing the higher-order relationship information among users becomes challenging and is often overlooked by most federated recommendation systems. To overcome these challenges, we propose a federated social recommendation approach with enhanced hypergraph neural network (HGNN). We introduce HGNN to learn user and item embeddings in federated recommendation systems, leveraging the hypergraph structure to address the heterogeneity of data. Based on carefully crafted triangular motifs, we merge user and item nodes to construct hypergraphs on local clients, capturing specific triangular relations. Multiple HGNN channels are used to encode different categories of high-order relations, and an attention mechanism is applied to aggregate the embedded information from these channels. Our experiments on real-world social recommendation datasets demonstrate the effectiveness of the proposed approach. Extensive experiment results on three publicly available datasets validate the effectiveness of the proposed method.

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