Search Results for author: Bing-Kun Bao

Found 8 papers, 5 papers with code

LD4MRec: Simplifying and Powering Diffusion Model for Multimedia Recommendation

no code implementations27 Sep 2023 Penghang Yu, Zhiyi Tan, Guanming Lu, Bing-Kun Bao

It maps the discrete behavior data into a continuous latent space, and generates behaviors with the guidance of collaborative signals and user multimodal preference.

Multimedia recommendation

MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting

no code implementations30 Aug 2023 Mingjie Qiu, Zhiyi Tan, Bing-Kun Bao

To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph.

Graph Learning

Multi-View Graph Convolutional Network for Multimedia Recommendation

2 code implementations7 Aug 2023 Penghang Yu, Zhiyi Tan, Guanming Lu, Bing-Kun Bao

Meanwhile, a behavior-aware fuser is designed to comprehensively model user preferences by adaptively learning the relative importance of different modality features.

Multimedia recommendation

UTM: A Unified Multiple Object Tracking Model With Identity-Aware Feature Enhancement

no code implementations CVPR 2023 Sisi You, Hantao Yao, Bing-Kun Bao, Changsheng Xu

Recently, Multiple Object Tracking has achieved great success, which consists of object detection, feature embedding, and identity association.

Multiple Object Tracking object-detection +1

DE-Net: Dynamic Text-guided Image Editing Adversarial Networks

1 code implementation2 Jun 2022 Ming Tao, Bing-Kun Bao, Hao Tang, Fei Wu, Longhui Wei, Qi Tian

To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) which composes different editing modules dynamically for various editing requirements.


DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering

1 code implementation10 Jul 2021 Jianyu Wang, Bing-Kun Bao, Changsheng Xu

However, existing graph-based methods fail to perform multi-step reasoning well, neglecting two properties of VideoQA: (1) Even for the same video, different questions may require different amount of video clips or objects to infer the answer with relational reasoning; (2) During reasoning, appearance and motion features have complicated interdependence which are correlated and complementary to each other.

Graph Attention Question Answering +3

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