Subhashini Venugopalan
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Long-term recurrent convolutional networks for visual recognition and description
J Donahue, L Anne Hendricks, S Guadarrama, M Rohrbach, ...
Proceedings of the IEEE conference on computer vision and pattern …, 2015
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
V Gulshan, L Peng, M Coram, MC Stumpe, D Wu, A Narayanaswamy, ...
jama 316 (22), 2402-2410, 2016
Sequence to sequence-video to text
S Venugopalan, M Rohrbach, J Donahue, R Mooney, T Darrell, K Saenko
Proceedings of the IEEE international conference on computer vision, 4534-4542, 2015
Translating videos to natural language using deep recurrent neural networks
S Venugopalan, H Xu, J Donahue, M Rohrbach, R Mooney, K Saenko
Proceedings of the 2015 Conference of the North American Chapter of the …, 2014
Detecting cancer metastases on gigapixel pathology images
Y Liu, K Gadepalli, M Norouzi, GE Dahl, T Kohlberger, A Boyko, ...
arXiv preprint arXiv:1703.02442, 2017
Youtube2text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition
S Guadarrama, N Krishnamoorthy, G Malkarnenkar, S Venugopalan, ...
Proceedings of the IEEE international conference on computer vision, 2712-2719, 2013
Deep compositional captioning: Describing novel object categories without paired training data
LA Hendricks, S Venugopalan, M Rohrbach, R Mooney, K Saenko, ...
Proceedings of the IEEE conference on computer vision and pattern …, 2016
Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild.
J Thomason, S Venugopalan, S Guadarrama, K Saenko, RJ Mooney
International Conference on Computational Linguistics (COLING) 2 (5), 9, 2014
Captioning images with diverse objects
S Venugopalan, LA Hendricks, M Rohrbach, R Mooney, T Darrell, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
Detection of anaemia from retinal fundus images via deep learning
A Mitani, A Huang, S Venugopalan, GS Corrado, L Peng, DR Webster, ...
Nature Biomedical Engineering 4 (1), 18-27, 2020
Improving lstm-based video description with linguistic knowledge mined from text
S Venugopalan, LA Hendricks, R Mooney, K Saenko
Empirical Methods in Natural Language Processing (EMNLP-16), 1961--1966, 2016
Predicting the risk of developing diabetic retinopathy using deep learning
A Bora, S Balasubramanian, B Babenko, S Virmani, S Venugopalan, ...
The Lancet Digital Health 3 (1), e10-e19, 2021
Multimodal video description
V Ramanishka, A Das, DH Park, S Venugopalan, LA Hendricks, ...
Proceedings of the 24th ACM international conference on Multimedia, 1092-1096, 2016
Topic based classification and pattern identification in patents
S Venugopalan, V Rai
Technological Forecasting and Social Change 94, 236-250, 2015
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
AV Varadarajan, P Bavishi, P Ruamviboonsuk, P Chotcomwongse, ...
Nature communications 11 (1), 130, 2020
Guided integrated gradients: An adaptive path method for removing noise
A Kapishnikov, S Venugopalan, B Avci, B Wedin, M Terry, T Bolukbasi
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
Is attention all that neRF needs?
P Wang, X Chen, T Chen, S Venugopalan, Z Wang
arXiv preprint arXiv:2207.13298, 2022
A multi-scale multiple instance video description network
H Xu, S Venugopalan, V Ramanishka, M Rohrbach, K Saenko
ICCV Workshop on Closing the Loop between Vision and Language, 2015
Attribution in scale and space
S Xu, S Venugopalan, M Sundararajan
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Semantic text summarization of long videos
S Sah, S Kulhare, A Gray, S Venugopalan, E Prud'Hommeaux, R Ptucha
2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 989-997, 2017
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