Haoying Sun

I am a Ph.D. student at Beijing University of Technology. My research focuses on sports video understanding, multimodal learning, video captioning, low-level vision, diffusion models, and knowledge graphs. I received my M.S. degree from Minzu University of China.

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Research

I'm interested in computer vision, deep learning, and image processing. Most of my research focuses on video understanding, especially video captioning and identity-aware sports video captioning. Previously, I worked on image restoration tasks, including image dehazing and digital restoration of Dunhuang murals. My master's thesis was centered on the digital restoration of Dunhuang murals.

This paper proposes a multi-modal knowledge framework (MK-VC) for video captioning that integrates dynamic context and static concept knowledge, with fine-grained adaptive fusion and attribute-guided alignment to enhance caption generation and handle long-tail word distribution.

We proposed UHCL, a unified hierarchical contrastive learning method for video captioning that enhances caption distinctiveness and overall performance, using triamese decoders and adaptive token fusion, achieving state-of-the-art results on the MSR-VTT and MSVD datasets.

We developed LLM-VC, a player-centric multimodal prompt generation network for identity-aware sports video captioning, which integrates visual and semantic cues to recognize player identities and generate accurate, player-specific descriptions, achieving state-of-the-art performance on the new NBA-Identity and VC-NBA-2022 datasets.

We proposed EIKA, an Entity-Aware Sports Video Captioning framework that integrates explicit player knowledge and implicit scene understanding to generate fine-grained, informative captions, achieving state-of-the-art results on multiple benchmark datasets.

We propose DSSM-KG, a dual-stream state-space model with cross-modal knowledge injection for video captioning. By integrating Transformer–Mamba hybrid modules for joint spatiotemporal modeling and adaptively injecting a commonsense-enhanced knowledge graph, DSSM-KG achieves competitive results on MSVD and MSRVTT.

This paper proposes ST2, a SpatioTemporal-enhanced State Space Model and Transformer for video captioning, which integrates Mamba and Transformer in parallel to achieve efficient spatiotemporal joint modeling, achieving competitive performance on MSVD and MSR-VTT datasets.

We proposed a VAE-GAN-based hybrid sample learning framework for image dehazing that jointly leverages synthetic and real data through latent space alignment and feature-adaptive fusion, achieving clearer dehazing results and higher PSNR on real-world hazy images.

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