Follow
Shyamal Buch
Shyamal Buch
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
42652021
SST: Single-stream temporal action proposals
S Buch, V Escorcia, C Shen, B Ghanem, J Carlos Niebles
Proceedings of the IEEE conference on Computer Vision and Pattern …, 2017
5212017
End-to-end, single-stream temporal action detection in untrimmed videos
S Buch, V Escorcia, B Ghanem, L Fei-Fei, JC Niebles
Proceedings of the British Machine Vision Conference (BMVC), 2017
2802017
iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes
B Shen*, F Xia*, C Li*, R Martín-Martín*, L Fan, G Wang, S Buch, ...
arXiv preprint arXiv:2012.02924, 2020
1622020
Revisiting the "Video" in Video-Language Understanding
S Buch, C Eyzaguirre, A Gaidon, J Wu, L Fei-Fei, JC Niebles
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
1582022
Behavior: Benchmark for everyday household activities in virtual, interactive, and ecological environments
S Srivastava, C Li, M Lingelbach, R Martín-Martín, F Xia, KE Vainio, Z Lian, ...
Conference on robot learning, 477-490, 2022
1492022
On the opportunities and risks of foundation models. arXiv
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
1122021
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos
DA Huang*, S Buch*, L Dery, A Garg, L Fei-Fei, J Carlos Niebles
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
1032018
On the opportunities and risks of foundation models. arXiv 2021
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2023
952023
RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition
L Fan*, S Buch*, G Wang, R Cao, Y Zhu, JC Niebles, L Fei-Fei
Proceedings of the European Conference on Computer Vision (ECCV), 2020
792020
On the opportunities and risks of foundation models (arXiv: 2108.07258). arXiv
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
782022
& Liang, P.(2021). On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 0
71
The activitynet large-scale activity recognition challenge 2018 summary
B Ghanem, JC Niebles, C Snoek, FC Heilbron, H Alwassel, V Escorcia, ...
arXiv preprint arXiv:1808.03766, 2018
682018
On the opportunities and risks of foundation models (2021)
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2022
622022
Activitynet challenge 2017 summary
B Ghanem, JC Niebles, C Snoek, FC Heilbron, H Alwassel, R Khrisna, ...
arXiv preprint arXiv:1710.08011, 2017
592017
End-to-end joint semantic segmentation of actors and actions in video
J Ji, S Buch, A Soto, JC Niebles
Proceedings of the European Conference on Computer Vision (ECCV), 702-717, 2018
522018
System and method for leveraging end-to-end driving models for improving driving task modules
SD Buch, AD Gaidon
US Patent 10,866,588, 2020
212020
Streaming dense video captioning
X Zhou*, A Arnab*, S Buch, S Yan, A Myers, X Xiong, A Nagrani, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024
152024
Neural event semantics for grounded language understanding
S Buch, L Fei-Fei, ND Goodman
Transactions of the Association for Computational Linguistics 9, 875-890, 2021
102021
MoReVQA: Exploring Modular Reasoning Models for Video Question Answering
J Min, S Buch, A Nagrani, M Cho, C Schmid
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024
72024
The system can't perform the operation now. Try again later.
Articles 1–20