Subhashini Venugopalan
TitleCited byYear
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
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
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
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
Deep compositional captioning: Describing novel object categories without paired training data
L Anne Hendricks, S Venugopalan, M Rohrbach, R Mooney, K Saenko, ...
Proceedings of the IEEE conference on computer vision and pattern …, 2016
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
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
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
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
Improved semantic parsers for if-then statements
I Beltagy, C Quirk
Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016
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
Unsupervised code-switching for multilingual historical document transcription
D Garrette, H Alpert-Abrams, T Berg-Kirkpatrick, D Klein
Proceedings of the 2015 Conference of the North American Chapter of the …, 2015
Utilizing large scale vision and text datasets for image segmentation from referring expressions
R Hu, M Rohrbach, S Venugopalan, T Darrell
arXiv preprint arXiv:1608.08305, 2016
Attribute based cryptology
S Venugopalan
M. Tech. Dissertation, 2011
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
A Varadarajan, P Bavishi, P Raumviboonsuk, P Chotcomwongse, ...
arXiv preprint arXiv:1810.10342, 2018
Natural-language video description with deep recurrent neural networks
S Venugopalan
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