Chaoya Jiang
Orcid: 0009-0009-7282-159X
According to our database1,
Chaoya Jiang
authored at least 17 papers
between 2020 and 2024.
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Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2024
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model.
CoRR, 2024
CoRR, 2024
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
CoRR, 2023
Vision Langauge Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation.
CoRR, 2023
COPA : Efficient Vision-Language Pre-training through Collaborative Object- and Patch-Text Alignment.
Proceedings of the 31st ACM International Conference on Multimedia, 2023
BUS : Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023
2022
TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022
2020
Learn A Robust Representation For Cover Song Identification Via Aggregating Local And Global Music Temporal Context.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2020
Similarity Learning For Cover Song Identification Using Cross-Similarity Matrices of Multi-Level Deep Sequences.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020