Sa2VA: Marrying SAM2 With MLLM for Dense Grounded Understanding of Images and Videos
- DOI
- 10.1109/tpami.2026.3720379
- Published
- 2026-11
- Container
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tpami.2026.3720379,
title = {Sa2VA: Marrying SAM2 With MLLM for Dense Grounded Understanding of Images and Videos},
author = {Haobo Yuan and Xiangtai Li and Tao Zhang and Yueyi Sun and Zilong Huang and Shilin Xu and Shunping Ji and Yunhai Tong and Lu Qi and Jiashi Feng and Ming-Hsuan Yang},
year = {2026},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/tpami.2026.3720379},
url = {https://doi.org/10.1109/tpami.2026.3720379}
}RIS
TY - JOUR TI - Sa2VA: Marrying SAM2 With MLLM for Dense Grounded Understanding of Images and Videos AU - Haobo Yuan AU - Xiangtai Li AU - Tao Zhang AU - Yueyi Sun AU - Zilong Huang AU - Shilin Xu AU - Shunping Ji AU - Yunhai Tong AU - Lu Qi AU - Jiashi Feng AU - Ming-Hsuan Yang PY - 2026 JO - IEEE Transactions on Pattern Analysis and Machine Intelligence DO - 10.1109/tpami.2026.3720379 UR - https://doi.org/10.1109/tpami.2026.3720379 ER -
APA
Yuan, H., Li, X., Zhang, T., Sun, Y., Huang, Z., Xu, S., Ji, S., Tong, Y., Qi, L., Feng, J., & Yang, M. (2026). Sa2VA: Marrying SAM2 With MLLM for Dense Grounded Understanding of Images and Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2026.3720379
Source records
- crossref · retrieved 2026-11-05T08:33:30.727Z