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Bo Yan

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20 papers

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AAAI Conference 2025 Conference Paper

Efficient Online Training for Zero-Shot Time-Lapse Microscopy Denoising and Super-Resolution

Ruian He
Ri Cheng
Xinkai Lyu
Weimin Tan
Bo Yan

In time-lapse microscopy, inherent noise significantly limits imaging sensitivity and increases measurement uncertainty. Due to the scarcity of clean data, zero-shot approaches have emerged as highly data-efficient solutions for microscopy denoising. However, existing methods typically process video frames independently, resulting in long training times and issues such as temporal noise and over-smoothing. In this paper, we introduce MDSR-Zero, a zero-shot online learning method designed for plug-and-play noise suppression and super-resolution of microscopy videos. Our approach leverages an efficient online training strategy that reuses denoising models from previous frames. By treating the video as a continuous stream, our model significantly reduces training time and ensures temporally consistent denoising. Additionally, we propose a novel loss function tailored for denoising in the context of super-resolution, which enhances the detail in the denoised results. Extensive experiments on both synthetic and real-world noise demonstrate that our method achieves state-of-the-art performance among zero-shot denoising approaches and is competitive with self-supervised methods. Notably, our method can reduce training time by up to 10x compared to the previous SOTA method.

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AAAI Conference 2025 Conference Paper

Federated Graph Condensation with Information Bottleneck Principles

Bo Yan
Sihao He
Cheng Yang
Shang Liu
Yang Cao
Chuan Shi

Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks. However, existing GC methods rely on centralized data storage, which is unfeasible for real-world decentralized data distribution, and overlook data holders' privacy-preserving requirements. To bridge this gap, we propose and study the novel problem of federated graph condensation (FGC) for graph neural networks (GNNs). Specifically, we first propose a general framework for FGC, where we decouple the typical gradient matching process for GC into client-side gradient calculation and server-side gradient matching, integrating knowledge from multiple clients' subgraphs into one smaller condensed graph. Nevertheless, our empirical studies show that under the federated setting, the condensed graph will consistently leak data membership privacy, i.e., the condensed graph during federated training can be utilized to steal training data under the membership inference attack (MIA). To tackle this issue, we innovatively incorporate information bottleneck principles into the FGC, which only needs to extract partial node features in one local pre-training step and utilize the features during federated training. Theoretical and experimental analyses demonstrate that our framework consistently protects membership privacy during training. Meanwhile, it can achieve comparable and even superior performance against existing centralized GC and federated graph learning (FGL) methods.

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AAAI Conference 2025 Conference Paper

Harnessing Language Model for Cross-Heterogeneity Graph Knowledge Transfer

Jinyu Yang
Ruijia Wang
Cheng Yang
Bo Yan
Qimin Zhou
Yang Juan
Chuan Shi

Heterogeneous graphs (HGs) that contain various node and edge types are ubiquitous in real-world scenarios. Considering the common label sparsity problem in HGs, some researchers propose to pretrain on source HGs to extract general knowledge and then fine-tune on a target HG for knowledge transfer. However, existing methods often assume that source and target HGs share a single heterogeneity, meaning that they have the same types of nodes and edges, which contradicts the real-world scenarios requiring cross-heterogeneity transfer. Although a recent study has made some preliminary attempts in cross-heterogeneity learning, its definition of general knowledge heavily rely on human knowledge, which lacks flexibility and further leads to a suboptimal transfer. To address the problem, we propose a novel Language Model-enhanced Cross-Heterogeneity learning model, namely LMCH. Specifically, we first design a metapath-based corpus construction method to unify HG representations as languages. The corpora of source HGs are then used to fine-tune a pretrained Language Model (LM), enabling the LM to autonomously extract general knowledge across different HGs. Furthermore, to fully utilize the extensive unlabeled nodes in a few-labeled target HG, we propose an iterative training pipeline with the help of an extra Graph Neural Network (GNN) predictor, enhanced by LM-GNN contrastive alignment at the end of each iteration. Extensive experiments on four real-world datasets have demonstrated the superior performance of LMCH over state-of-the-art methods.

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JBHI Journal 2025 Journal Article

IPLC+: SAM-Guided Iterative Pseudo Label Correction for Source-Free Domain Adaptation in Medical Image Segmentation

Guoning Zhang
Xiaoran Qi
Jianghao Wu
Bo Yan
Guotai Wang

Domain Adaptation (DA) is important for a segmentation model to deal with domain shift in a new target domain. Due to the privacy concern of medical data and the expensive annotation process, Source-Free Domain Adaptation (SFDA) is appealing without access to source data and labels of target domain images for the adaptation. However, existing SFDA methods have limited performance due to insufficient supervision and unreliable pseudo labels. In this paper, we propose an enhanced Iterative Pseudo Label Correction (IPLC+) SFDA framework guided by Segment Anything Model (SAM) for medical image segmentation. Specifically, with a pre-trained source model and SAM, we propose a Reliable SAM Pseudo-label Generator (RSPG) to obtain high-quality and reliable pseudo labels in the target domain based on multiple prompts randomly sampled from the model’s prediction. To provide more efficient constraints during adaptation, we introduce self-training pseudo labels weighted by the uncertainty, and propose regularization using mean curvature minimization based on shape-prior knowledge for smoother segmentation. We also propose an Iterative Correction Learning (ICL) strategy to iteratively refine pseudo labels using SAM with updated prompts and combine supervisions to optimize the model sufficiently. Experiments on two public multi-site datasets for prostate and heart segmentation show that our method effectively outperformed ten state-of-the-art SFDA methods, improved the quality of pseudo labels, and even achieved better results than fully supervised learning in the target domain in some cases.

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AAAI Conference 2025 Conference Paper

MHBench: Demystifying Motion Hallucination in VideoLLMs

Ming Kong
Xianzhou Zeng
Luyuan Chen
Yadong Li
Bo Yan
Qiang Zhu

Similar to Language or Image LLMs, VideoLLMs are also plagued by hallucination issues. Hallucinations in videos not only manifest in the spatial dimension regarding the perception of the existence of visual objects (static) but also the temporal dimension influencing the perception of actions and events (dynamic). This paper introduces the concept of Motion Hallucination for the first time, exploring the hallucination phenomena caused by insufficient motion perception capabilities in VideoLMMs, as well as how to detect, evaluate, and mitigate the hallucination. To this end, we propose the first benchmark for assessing motion hallucination MHBench, which consists of 1,200 videos of 20 different action categories. By constructing a collection of adversarial triplet types of videos (original/antonym/incomplete), we achieve a comprehensive evaluation of motion hallucination. Furthermore, we present a Motion Contrastive Decoding (MotionCD) method, which employs bidirectional motion elimination between the original video and its reverse playback to construct an amateur model that removes the influence of motion while preserving visual information, thereby effectively suppressing motion hallucination. Extensive experiments on MHBench reveal that current state-of-the-art VideoLLMs significantly suffer from motion hallucination, while the introduction of MotionCD can effectively mitigate this issue, achieving up to a 15.1% performance improvement. We hope this work will guide future efforts in avoiding and mitigating hallucinations in VideoLLMs.

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IJCAI Conference 2025 Conference Paper

Multimodal Inference with Incremental Tabular Attributes

Xinda Chen
Zhen Xing
Zixian Zhang
Weimin Tan
Bo Yan

Multimodal Learning with visual and tabular modalities has become more and more popular nowadays, especially in the healthcare area. Due to the adaptation of new equipment or new factors being introduced, the tabular modality keeps changing. However, the standard process of training multimodal AI models requires tables to have fixed columns in training and inference; thus, it is not suitable for handling dynamically changed tables. Therefore, new methods are needed for efficiently handling such tables in multimodal learning. In this paper, we introduce a new task, multimodal inference with incremental tabular attributes, which aims to enable trained multimodal models to leverage incremental attributes in tabular modality during the inference stage efficiently. We implement a specialized encoder to disentangle the latent representation of incremental tabular attributes inside itself and with the old attributes to reduce information redundancy and further align the incremental attributes with the visual modality with consistency loss to improve information richness. Experimental results across five public datasets show that our method effectively utilizes incremental tabular attributes, achieving state-of-the-art performance in general scenarios. Beyond the inference, we also find that our method achieved better performance in fully supervised settings, evoking a new training style for multimodal learning with tables.

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AAAI Conference 2025 Conference Paper

Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective

Zhongjian Zhang
Mengmei Zhang
Xiao Wang
Lingjuan Lyu
Bo Yan
Junping Du
Chuan Shi

To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively. Unlike FL, FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL. Recently, as an essential principle of FL, model security has received increasing attention, especially for Byzantine attacks, where malicious clients can send arbitrary updates. The problem of exploring the Byzantine robustness of FR is particularly critical since in the domains applying FR, e.g., e-commerce, malicious clients can be injected easily by registering new accounts. However, existing Byzantine works neglect the unique sparse aggregation of FR, making them unsuitable for our problem. Thus, we make the first effort to investigate Byzantine attacks on FR from the perspective of sparse aggregation, which is non-trivial: it is not clear how to define Byzantine robustness under sparse aggregations and design Byzantine attacks under limited knowledge/capability. In this paper, we reformulate the Byzantine robustness under sparse aggregation by defining the aggregation for a single item as the smallest execution unit. Then we propose a family of effective attack strategies, named Spattack, which exploit the vulnerability in sparse aggregation and are categorized along the adversary's knowledge and capability. Extensive experimental results demonstrate that Spattack can effectively prevent convergence and even break down defenses under a few malicious clients, raising alarms for securing FR systems.

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AAAI Conference 2024 Conference Paper

Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation

Ri Cheng
Ruian He
Xuhao Jiang
Shili Zhou
Weimin Tan
Bo Yan

Existing recurrent optical flow estimation networks are computationally expensive since they use a fixed large number of iterations to update the flow field for each sample. An efficient network should skip iterations when the flow improvement is limited. In this paper, we develop a Context-Aware Iteration Policy Network for efficient optical flow estimation, which determines the optimal number of iterations per sample. The policy network achieves this by learning contextual information to realize whether flow improvement is bottlenecked or minimal. On the one hand, we use iteration embedding and historical hidden cell, which include previous iterations information, to convey how flow has changed from previous iterations. On the other hand, we use the incremental loss to make the policy network implicitly perceive the magnitude of optical flow improvement in the subsequent iteration. Furthermore, the computational complexity in our dynamic network is controllable, allowing us to satisfy various resource preferences with a single trained model. Our policy network can be easily integrated into state-of-the-art optical flow networks. Extensive experiments show that our method maintains performance while reducing FLOPs by about 40%/20% for the Sintel/KITTI datasets.

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AAAI Conference 2024 Conference Paper

Low-Latency Space-Time Supersampling for Real-Time Rendering

Ruian He
Shili Zhou
Yuqi Sun
Ri Cheng
Weimin Tan
Bo Yan

With the rise of real-time rendering and the evolution of display devices, there is a growing demand for post-processing methods that offer high-resolution content in a high frame rate. Existing techniques often suffer from quality and latency issues due to the disjointed treatment of frame supersampling and extrapolation. In this paper, we recognize the shared context and mechanisms between frame supersampling and extrapolation, and present a novel framework, Space-time Supersampling (STSS). By integrating them into a unified framework, STSS can improve the overall quality with lower latency. To implement an efficient architecture, we treat the aliasing and warping holes unified as reshading regions and put forth two key components to compensate the regions, namely Random Reshading Masking (RRM) and Efficient Reshading Module (ERM). Extensive experiments demonstrate that our approach achieves superior visual fidelity compared to state-of-the-art (SOTA) methods. Notably, the performance is achieved within only 4ms, saving up to 75\% of time against the conventional two-stage pipeline that necessitates 17ms.

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AAAI Conference 2024 Conference Paper

Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables

Yang Chen
Xiao Lin
Bo Yan
Libo Zhang
Jiamou Liu
Neset Özkan Tan
Michael Witbrock

Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations. While promising, the assumption of agent homogeneity limits the capability of existing methods to handle demonstrations with heterogeneous and unknown objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method. Critically, our method can infer rewards from different yet structurally similar tasks without prior knowledge about underlying contexts or modifying the MFG model itself. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs.

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AAAI Conference 2024 Conference Paper

MGQFormer: Mask-Guided Query-Based Transformer for Image Manipulation Localization

Kunlun Zeng
Ri Cheng
Weimin Tan
Bo Yan

Deep learning-based models have made great progress in image tampering localization, which aims to distinguish between manipulated and authentic regions. However, these models suffer from inefficient training. This is because they use ground-truth mask labels mainly through the cross-entropy loss, which prioritizes per-pixel precision but disregards the spatial location and shape details of manipulated regions. To address this problem, we propose a Mask-Guided Query-based Transformer Framework (MGQFormer), which uses ground-truth masks to guide the learnable query token (LQT) in identifying the forged regions. Specifically, we extract feature embeddings of ground-truth masks as the guiding query token (GQT) and feed GQT and LQT into MGQFormer to estimate fake regions, respectively. Then we make MGQFormer learn the position and shape information in ground-truth mask labels by proposing a mask-guided loss to reduce the feature distance between GQT and LQT. We also observe that such mask-guided training strategy has a significant impact on the convergence speed of MGQFormer training. Extensive experiments on multiple benchmarks show that our method significantly improves over state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model

Shili Zhou
Ruian He
Weimin Tan
Bo Yan

Optical Flow Estimation aims to find the 2D dense motion field between two frames. Due to the limitation of model structures and training datasets, existing methods often rely too much on local clues and ignore the integrity of objects, resulting in fragmented motion estimation. Through theoretical analysis, we find the pre-trained large vision models are helpful in optical flow estimation, and we notice that the recently famous Segment Anything Model (SAM) demonstrates a strong ability to segment complete objects, which is suitable for solving the fragmentation problem. We thus propose a solution to embed the frozen SAM image encoder into FlowFormer to enhance object perception. To address the challenge of in-depth utilizing SAM in non-segmentation tasks like optical flow estimation, we propose an Optical Flow Task-Specific Adaption scheme, including a Context Fusion Module to fuse the SAM encoder with the optical flow context encoder, and a Context Adaption Module to adapt the SAM features for optical flow task with Learned Task-Specific Embedding. Our proposed SAMFlow model reaches 0.86/2.10 clean/final EPE and 3.55/12.32 EPE/F1-all on Sintel and KITTI-15 training set, surpassing Flowformer by 8.5%/9.9% and 13.2%/16.3%. Furthermore, our model achieves state-of-the-art performance on the Sintel and KITTI-15 benchmarks, ranking #1 among all two-frame methods on Sintel clean pass.

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IJCAI Conference 2023 Conference Paper

Learning Survival Distribution with Implicit Survival Function

Yu Ling
Weimin Tan
Bo Yan

Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time space for likelihood estimation with censorship, which leads to weak generalization. In this paper, we propose Implicit Survival Function (ISF) based on Implicit Neural Representation for survival distribution estimation without strong assumptions, and employ numerical integration to approximate the cumulative distribution function for prediction and optimization. Experimental results show that ISF outperforms the state-of-the-art methods in three public datasets and has robustness to the hyperparameter controlling estimation precision.

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AAAI Conference 2023 Conference Paper

Multi-Modality Deep Network for Extreme Learned Image Compression

Xuhao Jiang
Weimin Tan
Tian Tan
Bo Yan
Liquan Shen

Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years, but suffer from blur and severe semantics loss at extremely low bitrates. To address this issue, we propose a multimodal machine learning method for text-guided image compression, in which the semantic information of text is used as prior information to guide image compression for better compression performance. We fully study the role of text description in different components of the codec, and demonstrate its effectiveness. In addition, we adopt the image-text attention module and image-request complement module to better fuse image and text features, and propose an improved multimodal semantic-consistent loss to produce semantically complete reconstructions. Extensive experiments, including a user study, prove that our method can obtain visually pleasing results at extremely low bitrates, and achieves a comparable or even better performance than state-of-the-art methods, even though these methods are at 2x to 4x bitrates of ours.

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IJCAI Conference 2023 Conference Paper

Multi-Modality Deep Network for JPEG Artifacts Reduction

Xuhao Jiang
Weimin Tan
Qing Lin
Chenxi Ma
Bo Yan
Liquan Shen

In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for extreme low-bitrate image compression artifacts reduction. The main challenge is that the highly compressed image loses too much information, resulting in reconstructing high-quality image difficultly. To address this issue, we propose a multimodal fusion learning method for text-guided JPEG artifacts reduction, in which the corresponding text description not only provides the potential prior information of the highly compressed image, but also serves as supplementary information to assist in image deblocking. We fuse image features and text semantic features from the global and local perspectives respectively, and design a contrastive loss built upon contrastive learning to produce visually pleasing results. Extensive experiments, including a user study, prove that our method can obtain better deblocking results compared to the state-of-the-art methods.

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AAAI Conference 2022 Conference Paper

Promoting Single-Modal Optical Flow Network for Diverse Cross-Modal Flow Estimation

Shili Zhou
Weimin Tan
Bo Yan

In recent years, optical flow methods develop rapidly, achieving unprecedented high performance. Most of the methods only consider single-modal optical flow under the wellknown brightness-constancy assumption. However, in many application systems, images of different modalities need to be aligned, which demands to estimate cross-modal flow between the cross-modal image pairs. A lot of cross-modal matching methods are designed for some specific crossmodal scenarios. We argue that the prior knowledge of the advanced optical flow models can be transferred to the crossmodal flow estimation, which may be a simple but unified solution for diverse cross-modal matching tasks. To verify our hypothesis, we design a self-supervised framework to promote the single-modal optical flow networks for diverse corss-modal flow estimation. Moreover, we add a Cross- Modal-Adapter block as a plugin to the state-of-the-art optical flow model RAFT for better performance in cross-modal scenarios. Our proposed Modality Promotion Framework and Cross-Modal Adapter have multiple advantages compared to the existing methods. The experiments demonstrate that our method is effective on multiple datasets of different crossmodal scenarios.

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AAAI Conference 2019 Conference Paper

Frame and Feature-Context Video Super-Resolution

Bo Yan
Chuming Lin
Weimin Tan

For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion or recurrently exploit the previously estimated HR frames to super-resolve the following frame. The main weaknesses of these approaches are: 1) separately generating each output frame may obtain high-quality HR estimates while resulting in unsatisfactory flickering artifacts, and 2) combining previously generated HR frames can produce temporally consistent results in the case of short information flow, but it will cause significant jitter and jagged artifacts because the previous super-resolving errors are constantly accumulated to the subsequent frames.In this paper, we propose a fully end-to-end trainable frame and feature-context video super-resolution (FFCVSR) network that consists of two key sub-networks: local network and context network, where the first one explicitly utilizes a sequence of consecutive LR frames to generate local feature and local SR frame, and the other combines the outputs of local network and the previously estimated HR frames and features to super-resolve the subsequent frame. Our approach takes full advantage of the inter-frame information from multiple LR frames and the context information from previously predicted HR frames, producing temporally consistent highquality results while maintaining real-time speed by directly reusing previous features and frames. Extensive evaluations and comparisons demonstrate that our approach produces state-of-the-art results on a standard benchmark dataset, with advantages in terms of accuracy, efficiency, and visual quality over the existing approaches.

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AAMAS Conference 2019 Conference Paper

On the Maximization of Influence Over an Unknown Social Network

Bo Yan
Kexiu Song
Jiamou Liu
Fanku Meng
Yiping Liu
Hongyi Su

Influence maximization is a well-investigated problem which asks for key individuals who have significant influence in a given social network. This paper addresses this problem when the social network structure is hidden. We adopt the framework of influence learning from samples and build a neural network model to represent the information diffusion process. Based on the model, we propose two new algorithms NeuGreedy and NeuMax. NeuGreedy simulates the traditional greedy algorithm whilst NeuMax utilizes the weights of connections between neurons. We test the algorithms on both synthetic and real-world datasets. The results verify the effectiveness of the proposed methods as compared to existing algorithms with or without the network structure.

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AAMAS Conference 2018 Conference Paper

Balancing the Pain and Gain of Hobnobbing: Utility-Based Network Building over Atributed Social Networks

Yijin Cai
Hong Zheng
Jiamou Liu
Bo Yan
Hongyi Su
Yiping Liu

The establishment of interpersonal ties is a pivotal problem in the structural analysis of social networks. In particular, link recommendation problem asks for valuable future links to establish by an individual. Existing methods for this problem rely on link prediction that evaluates the likelihood of successful tie creation between two individuals. Such methods do not consider the social capital gained by agents, nor do they concern with the required cost of this process. In light of this limitation, we propose a utility-based network building problem, with an aim to strike a balance between the gained social capital – in the form of closeness centrality – and the cost of establishing ties. We propose algorithms to solve this problem over networks whose nodes may or may not be labelled with attributes, and test their performance on a range of synthesized and real-world social networks. By having multiple agents adopting utility-based network building strategies, we propose a suite of models of network formation and demonstrate empirically that the they capture important structural properties. In particular, we investigate the emergence of a core/periphery structure as a joint result of preferential attachment and network building strategies.

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IJCAI Conference 2018 Conference Paper

From the Periphery to the Core: Information Brokerage in an Evolving Network

Bo Yan
Yiping Liu
Jiamou Liu
Yijin Cai
Hongyi Su
Hong Zheng

Interpersonal ties are pivotal to individual efficacy, status and performance in an agent society. This paper explores three important and interrelated themes in social network theory: the center/periphery partition of the network; network dynamics; and social integration of newcomers. We tackle the question: How would a newcomer harness information brokerage to integrate into a dynamic network going from periphery to center? We model integration as the interplay between the newcomer and the dynamics network and capture information brokerage using a process of relationship building. We analyze theoretical guarantees for the newcomer to reach the center through tactics; proving that a winning tactic always exists for certain types of network dynamics. We then propose three tactics and show their superior performance over alternative methods on four real-world datasets and four network models. In general, our tactics place the newcomer to the center by adding very few new edges on dynamic networks with ~14000 nodes.

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