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Hongbin Sun

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8 papers

1 author row

AAAI Conference 2026 Conference Paper

Expand Your SCOPE: Semantic Cognition over Potential-Based Exploration for Embodied Visual Navigation

Ningnan Wang
Weihuang Chen
Liming Chen
Haoxuan Ji
Zhongyu Guo
Xuchong Zhang
Hongbin Sun

Embodied visual navigation remains a challenging task, as agents must explore unknown environments with limited knowledge. Existing zero-shot studies have shown that incorporating memory mechanisms to support goal-directed behavior can improve long-horizon planning performance. However, they overlook visual frontier boundaries, which fundamentally dictate future trajectories and observations, and fall short of inferring the relationship between partial visual observations and navigation goals. In this paper, we propose Semantic Cognition Over Potential-based Exploration (SCOPE), a zero-shot framework that explicitly leverages frontier information to drive potential-based exploration, enabling more informed and goal-relevant decisions. SCOPE estimates exploration potential with a Vision-Language Model and organizes it into a spatio-temporal potential graph, capturing boundary dynamics to support long-horizon planning. In addition, SCOPE incorporates a self-reconsideration mechanism that revisits and refines prior decisions, enhancing reliability and reducing overconfident errors. Experimental results on two diverse embodied navigation tasks show that SCOPE outperforms state-of-the-art baselines by 4.6% in accuracy. Further analysis demonstrates that its core components lead to improved calibration, stronger generalization, and higher decision quality.

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NeurIPS Conference 2024 Conference Paper

Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms

Chengyuan Deng
Jie Gao
Kevin Lu
Feng Luo
Hongbin Sun
Cheng Xin

We introduce \textbf{N}on-\textbf{Euc}lidean-\textbf{MDS} (Neuc-MDS), which extends Multidimensional Scaling (MDS) to generate outputs that can be non-Euclidean and non-metric. The main idea is to generalize the inner product to other symmetric bilinear forms to utilize the negative eigenvalues of dissimiliarity Gram matrices. Neuc-MDS efficiently optimizes the choice of (both positive and negative) eigenvalues of the dissimilarity Gram matrix to reduce STRESS, the sum of squared pairwise error. We provide an in-depth error analysis and proofs of the optimality in minimizing lower bounds of STRESS. We demonstrate Neuc-MDS's ability to address limitations of classical MDS raised by prior research, and test it on various synthetic and real-world datasets in comparison with both linear and non-linear dimension reduction methods.

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NeurIPS Conference 2023 Conference Paper

Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

Cheng Cheng
Lin Song
Ruoyi Xue
Hang Wang
Hongbin Sun
Yixiao Ge
Ying Shan

The contrastive vision-language pre-training, known as CLIP, demonstrates remarkable potential in perceiving open-world visual concepts, enabling effective zero-shot image recognition. Nevertheless, few-shot learning methods based on CLIP typically require offline fine-tuning of the parameters on few-shot samples, resulting in longer inference time and the risk of overfitting in certain domains. To tackle these challenges, we propose the Meta-Adapter, a lightweight residual-style adapter, to refine the CLIP features guided by the few-shot samples in an online manner. With a few training samples, our method can enable effective few-shot learning capabilities and generalize to unseen data or tasks without additional fine-tuning, achieving competitive performance and high efficiency. Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3. 6\% on eight image classification datasets with higher inference speed. Furthermore, our model is simple and flexible, serving as a plug-and-play module directly applicable to downstream tasks. Without further fine-tuning, Meta-Adapter obtains notable performance improvements in open-vocabulary object detection and segmentation tasks.

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NeurIPS Conference 2021 Conference Paper

Dynamic Grained Encoder for Vision Transformers

Lin Song
Songyang Zhang
Songtao Liu
Zeming Li
Xuming He
Hongbin Sun
Jian Sun
Nanning Zheng

Transformers, the de-facto standard for language modeling, have been recently applied for vision tasks. This paper introduces sparse queries for vision transformers to exploit the intrinsic spatial redundancy of natural images and save computational costs. Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region. Thus it achieves a fine-grained representation in discriminative regions while keeping high efficiency. Besides, the dynamic grained encoder is compatible with most vision transformer frameworks. Without bells and whistles, our encoder allows the state-of-the-art vision transformers to reduce computational complexity by 40%-60% while maintaining comparable performance on image classification. Extensive experiments on object detection and segmentation further demonstrate the generalizability of our approach. Code is available at https: //github. com/StevenGrove/vtpack.

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YNICL Journal 2020 Journal Article

Abnormal neurite orientation dispersion and density imaging of white matter in children with primary nocturnal enuresis

Hongbin Sun
Bing Xue
Miao Peng
Hongwei Ma
Bing Yu
Yang Hou
Qiyong Guo

Several lines of evidence indicate that multiple abnormalities of gray matter are related to the pathogenesis of primary nocturnal enuresis (PNE); however, few studies have been conducted with respect to abnormalities in white matter (WM) of children with PNE. The present work investigated the microstructure of WM in children with PNE using a neurite orientation dispersion and density imaging (NODDI) method. NODDI data were obtained from 29 children with PNE (age = 9.8 ± 1.2 years, 59% males) and 34 healthy controls (age = 10.3 ± 1.6 years, 56% males) in this study. Multi-b-value diffusion-weighted imaging data were acquired with a 3 T MR system, and the orientation dispersion index (ODI) and neurite density index (NDI) maps were calculated. Tract-Based Spatial Statistics analyses of WM tracts were performed with ODI and NDI maps in children with PNE and controls. Children with PNE had lower ODIs in WM fiber tracts of the bilateral superior longitudinal fasciculus (SLF) and higher ODIs in the bilateral internal capsule (IC) and right anterior thalamic radiation (ATR) than controls. PNE children also had lower NDIs in the bilateral IC and the cingulum and higher NDIs in the bilateral SLF. These changes in NODDI indices, which indicated abnormal neural maturation of the WM microstructures, may be related to abnormal sleep and enuresis in children with PNE.

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NeurIPS Conference 2020 Conference Paper

Fine-Grained Dynamic Head for Object Detection

Lin Song
Yanwei Li
Zhengkai Jiang
Zeming Li
Hongbin Sun
Jian Sun
Nanning Zheng

The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, this strategy ignores the distinct characteristics of different sub-regions in an instance. To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation. Moreover, we design a spatial gate with the new activation function to reduce computational complexity dramatically through spatially sparse convolutions. Extensive experiments demonstrate the effectiveness and efficiency of the proposed method on several state-of-the-art detection benchmarks. Code is available at https: //github. com/StevenGrove/DynamicHead.

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NeurIPS Conference 2020 Conference Paper

Rethinking Learnable Tree Filter for Generic Feature Transform

Lin Song
Yanwei Li
Zhengkai Jiang
Zeming Li
Xiangyu Zhang
Hongbin Sun
Jian Sun
Nanning Zheng

The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces it to focus on the regions with close spatial distance, hindering the effective long-range interactions. To relax the geometric constraint, we give the analysis by reformulating it as a Markov Random Field and introduce a learnable unary term. Besides, we propose a learnable spanning tree algorithm to replace the original non-differentiable one, which further improves the flexibility and robustness. With the above improvements, our method can better capture long range dependencies and preserve structural details with linear complexity, which is extended to several vision tasks for more generic feature transform. Extensive experiments on object detection/instance segmentation demonstrate the consistent improvements over the original version. For semantic segmentation, we achieve leading performance (82. 1% mIoU) on the Cityscapes benchmark without bells-and whistles. Code is available at https: //github. com/StevenGrove/LearnableTreeFilterV2.

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NeurIPS Conference 2019 Conference Paper

Learnable Tree Filter for Structure-preserving Feature Transform

Lin Song
Yanwei Li
Zeming Li
Gang Yu
Hongbin Sun
Jian Sun
Nanning Zheng

Learning discriminative global features plays a vital role in semantic segmentation. And most of the existing methods adopt stacks of local convolutions or non-local blocks to capture long-range context. However, due to the absence of spatial structure preservation, these operators ignore the object details when enlarging receptive fields. In this paper, we propose the learnable tree filter to form a generic tree filtering module that leverages the structural property of minimal spanning tree to model long-range dependencies while preserving the details. Furthermore, we propose a highly efficient linear-time algorithm to reduce resource consumption. Thus, the designed modules can be plugged into existing deep neural networks conveniently. To this end, tree filtering modules are embedded to formulate a unified framework for semantic segmentation. We conduct extensive ablation studies to elaborate on the effectiveness and efficiency of the proposed method. Specifically, it attains better performance with much less overhead compared with the classic PSP block and Non-local operation under the same backbone. Our approach is proved to achieve consistent improvements on several benchmarks without bells-and-whistles. Code and models are available at https: //github. com/StevenGrove/TreeFilter-Torch.

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