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Bing-Kun Bao

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8 papers

1 author row

AAAI Conference 2025 Conference Paper

BeFA: A General Behavior-driven Feature Adapter for Multimedia Recommendation

Qile Fan
Penghang Yu
Zhiyi Tan
Bing-Kun Bao
Guanming Lu

Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them with behavioral features. However, pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details.We speculate that it may result in the extracted features not containing sufficient features to accurately reflect user preferences. To verify our hypothesis, we introduce an attribution analysis method for visually and intuitively analyzing the content features. The results indicate that certain items’ content features exhibit the issues of information drift and information omission, reducing the expressive ability of features. Building upon this finding, we propose an effective and efficient general Behaviordriven Feature Adapter (BeFA) to tackle these issues. This adapter reconstructs the content feature with the guidance of behavioral information, enabling content features accurately reflecting user preferences. Extensive experiments demonstrate the effectiveness of the adapter across all multimedia recommendation methods.

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IJCAI Conference 2025 Conference Paper

DToMA: Training-free Dynamic Token MAnipulation for Long Video Understanding

Bowen Yuan
Sisi You
Bing-Kun Bao

Video Large Language Models (VideoLLMs) often require thousands of visual tokens to process long videos, leading to substantial computational costs, further exacerbated by visual token inefficiency. Existing token reduction and alternative video representation methods improve efficiency but often compromise comprehension abilities. In this work, we analyze the reasoning processes of VideoLLMs in multi-choice VideoQA task, identifying three reasoning stages—shallow, intermediate, and deep stages—that closely mimic human cognitive processing. Our analysis reveals specific inefficiencies at each stage: in shallow layers, VideoLLMs attempt to memorize all video details without prioritizing relevant content; in intermediate layers, models fail to re-examine uncertain content dynamically; and in deep layers, they continue processing video even when sufficiently confident. To bridge this gap, we propose DToMA, a training-free Dynamic Token MAnipulation method inspired by human adjustment mechanisms in three aspects: 1) Text-guided keyframe-aware reorganization to prioritize keyframes and reduce redundancy, 2) Uncertainty-based visual injection to revisit content dynamically, and 3) Early-exit pruning to halt visual tokens when confident. Experiments on 6 long video understanding benchmarks show that DToMA enhances both efficiency and comprehension, outperforming state-of-the-art methods and generalizing well across 3 VideoLLM architectures and sizes. Code is available at https: //github. com/yuanrr/DToMA.

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IJCAI Conference 2025 Conference Paper

Graph Prompts: Adapting Video Graph for Video Question Answering

Yiming Li
Xiaoshan Yang
Bing-Kun Bao
Changsheng Xu

Due to the dynamic nature in videos, it is evident that perceiving and reasoning about temporal information are the key focus of Video Question Answering (VideoQA). In recent years, several methods have explored relationship-level temporal modeling with graph-structured video representation. Unfortunately, these methods heavily rely on the question text, thus making it challenging to perceive and reason about video content that is not explicitly mentioned in the question. To address the above challenge, we propose Graph Prompts-based VideoQA (GP-VQA), which adopts a video-based graph structure for enhanced video understanding. The proposed GP-VQA contains two stages, i. e. , pre-training and prompt tuning. In pre-training, we define the pretext task that requires GP-VQA to reason about the randomly masked nodes or edges in the video graph, thus prompting GP-VQA to learn the reasoning ability with video-guided information. In prompt-tuning, we organize the textual question into question graph and implement message passing from video graph to question graph, therefore inheriting the video-based reasoning ability from video graph completion to VideoQA. Extensive experiments on various datasets have demonstrated the promising performance of GP-VQA.

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AAAI Conference 2025 System Paper

InstantPainting: Expanding GANs for Efficient Text-Conditioned Image Generation Platform

Bing-Kun Bao
Yefei Sheng
Jie Wang
Yaning Li
Sisi You

Text-conditioned image generation enables cross-modal comprehension. Recent emergence of many platforms have found applications in diverse domains like assisted designing and video gaming. However, there still exist challenges in existing platforms due to their expensive training and time-consuming generation processes. In this paper, we introduce an efficient text-conditioned image generation platform, termed InstantPainting. Unlike existing platforms based on large-scale pre-trained diffusion models, InstantPainting expands generative adversarial networks (GANs) to achieve efficient generation by using only about three percent pre-training data of other platforms. Compared to existing platforms, InstantPainting achieves the following functions at a very low deployment cost and approximately 4 to 5 times faster generation speeds: (1) Multi-category and multi-size image generation (2) Image stylization and controlled generation (3) Creative generation, including the generation of poetry pictures and counterfactual images. The proposed platform provides web application implementations for PC and mobile, users can create high-quality images directly through the user interface.

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AAAI Conference 2025 Conference Paper

Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression

Ruizhi Pu
Gezheng Xu
Ruiyi Fang
Bing-Kun Bao
Charles Ling
Boyu Wang

Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning. While recent works have already shown that incorporating various classification-based regularizers can produce enhanced outcomes, the role of classification remains elusive in DIR. Moreover, such regularizers (e.g., contrastive penalties) merely focus on learning discriminative features of data, which inevitably results in ignorance of either continuity or similarity across the data. To address these issues, we first bridge the connection between the objectives of DIR and classification from a Bayesian perspective. Consequently, this motivates us to decompose the objective of DIR into a combination of classification and regression tasks, which naturally guides us toward a divide-and-conquer manner to solve the DIR problem. Specifically, by aggregating the data at nearby labels into the same groups, we introduce an ordinal group-aware contrastive learning loss along with a multi-experts regressor to tackle the different groups of data thereby maintaining the data continuity. Meanwhile, considering the similarity between the groups, we also propose a symmetric descending soft labeling strategy to exploit the intrinsic similarity across the data, which allows classification to facilitate regression more effectively. Extensive experiments on real-world datasets also validate the effectiveness of our method.

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AAAI Conference 2025 Conference Paper

Mind Individual Information! Principal Graph Learning for Multimedia Recommendation

Penghang Yu
Zhiyi Tan
Guanming Lu
Bing-Kun Bao

Graph Neural Network (GNN)-based methods have recently emerged as effective approaches for multimedia recommendation. Typically, these methods employ message passing on the user-item interaction graph, and model user preferences by exploiting co-occurrence patterns. Despite their effectiveness, we argue that they insufficiently exploit the individual information, potentially limiting recommendation performance. To validate our argument, we first analyze existing methods from spectral graph theory. We identify that existing methods focus on capturing global structural features, but underutilize local structural features that convey individual information. Further detailed experiments reveal that such an underutilization leads to overly similar user preferences modeling. Furthermore, we propose a novel Principal Graph Learning (PGL) framework to address this issue. The idea is to enhance user preference modeling by effectively mining and utilizing principal local structural features. PGL first extracts the principal subgraph from the user-item interaction graph using two novel extraction operators: global-aware and local-aware subgraph extraction. It then employs message passing on the principal subgraph to comprehensively model user perference, with the aim of simultaneously capturing co-occurrence patterns and individual information. Compared to existing methods, PGL achieves an average performance improvement of 9%.

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IJCAI Conference 2025 Conference Paper

SCVBench: A Benchmark with Multi-turn Dialogues for Story-Centric Video Understanding

Sisi You
Bowen Yuan
Bing-Kun Bao

Video understanding seeks to enable machines to interpret visual content across three levels: action, event, and story. Existing models are limited in their ability to perform high-level long-term story understanding, due to (1) the oversimplified treatment of temporal information and (2) the training bias introduced by action/event-centric datasets. To address this, we introduce SCVBench, a novel benchmark for story-centric video understanding. SCVBench evaluates LVLMs through an event ordering task decomposed into sub-questions leading to a final question, quantitatively measuring historical dialogue exploration. We collected 1, 253 final questions and 6, 027 sub-question pairs from 925 videos, constructing continuous multi-turn dialogues. Experimental results show that while closed-source GPT-4o outperforms other models, most open-source LVLMs struggle with story-centric video understanding. Additionally, our StoryCoT model significantly surpasses open-source LVLMs on SCVBench. SCVBench aims to advance research by comprehensively analyzing LVLMs' temporal reasoning and comprehension capabilities. Code can be accessed at https: //github. com/yuanrr/SCVBench.

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AAAI Conference 2023 Conference Paper

DE-net: Dynamic Text-Guided Image Editing Adversarial Networks

Ming Tao
Bing-Kun Bao
Hao Tang
Fei Wu
Longhui Wei
Qi Tian

Text-guided image editing models have shown remarkable results. However, there remain two problems. First, they employ fixed manipulation modules for various editing requirements (e.g., color changing, texture changing, content adding and removing), which results in over-editing or insufficient editing. Second, they do not clearly distinguish between text-required and text-irrelevant parts, which leads to inaccurate editing. To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) that composes different editing modules dynamically for various editing requirements. (ii) a Composition Predictor (Comp-Pred), which predicts the composition weights for DEBlock according to the inference on target texts and source images. (iii) a Dynamic text-adaptive Convolution Block (DCBlock) that queries source image features to distinguish text-required parts and text-irrelevant parts. Extensive experiments demonstrate that our DE-Net achieves excellent performance and manipulates source images more correctly and accurately.

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