The ACM on Web Conference (WWW) at Singapore (2024)
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publications
SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant
Published in Proceedings of the 18th European Conference on Computer Vision (ECCV), 2024
This work has introduced a new training method that enhances general-purpose vision-language understanding and image-oriented question answering through visual self-questioning.
Recommended citation: Sun, G., Qin, C., Wang, J., Chen, Z., Xu, R., & Tao, Z. (2024). SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant. ECCV
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STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical
Published in Empirical Methods in Natural Language Processing (EMNLP), 2024
This work have introduced a novel self-training approach to enhance the data efficiency of training LVLMs for medical tasks
Recommended citation: Sun, G., Qin, C., Fu, H., Wang, L., & Tao, Z. (2024). STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical.
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Aligning Out-of-Distribution Web Images and Caption Semantics via Evidential Learning
Published in Proceedings of the ACM on Web Conference (WWW), 2024
This work efficiently improve the pre-trained vision-language networks in terms of robustness and performance when handling ID and OOD cases in image-text retrieval tasks via evidence knowledge.
Recommended citation: Guohao Sun, Yue Bai, Xueying Yang, Yi Fang, Yun Fu, and Zhiqiang Tao. 2024. Aligning Out-of-Distribution Web Images and Caption Semantics via Evidential Learning. WWW.
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Prototypical Transformer as Unified Motion Learners
Published in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
This work refines the feature representations via prototype-feature association
Recommended citation: Han, C., Lu, Y., Sun, G., Liang, J., Cao, Z., Wang, Q., Guan, Q., Dianat, S.A., Rao, R.M., Geng, T., Tao, Z., & Liu, D. (2024). Prototypical Transformer as Unified Motion Learners. ICML
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Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval
Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
We introduced T-MASS, where text is modeled as a stochastic embedding, facilitating joint learning of the text mass and video points.
Recommended citation: Wang, J., Sun, G., Wang, P., Liu, D., Dianat, S.A., Rabbani, M., Rao, R.M., & Tao, Z. (2024). Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval. CVPR
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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