Jason J. Corso
University of Michigan
269 Papers
1.8K Citations
Jason J. Corso is an academic researcher from University of Michigan. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 41, co-authored 265 publications. Previous affiliations of Jason J. Corso include Johns Hopkins University & State University of New York System.
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Papers
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Unified Vision-Language Pre-Training for Image Captioning and VQA
Luowei Zhou,Hamid Palangi,Lei Zhang,Houdong Hu,Jason J. Corso,Jianfeng Gao +5 more
- 03 Apr 2020
TL;DR: VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions and VQA 2.0.
Action bank: A high-level representation of activity in video
Sreemanananth Sadanand,Jason J. Corso +1 more
- 16 Jun 2012
TL;DR: Action bank as discussed by the authors is composed of many individual action detectors sampled broadly in semantic space as well as viewpoint space, which is constructed to be semantically rich and even when paired with simple linear SVM classifiers is capable of highly discriminative performance.
•Posted Content
Towards Automatic Learning of Procedures from Web Instructional Videos
TL;DR: A segment-level recurrent network is proposed for generating procedure segments by modeling the dependencies across segments and it is shown that the proposed model outperforms competitive baselines in procedure segmentation.
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End-to-End Dense Video Captioning with Masked Transformer
Luowei Zhou,Yingbo Zhou,Jason J. Corso,Richard Socher,Caiming Xiong +4 more
- 03 Apr 2018
TL;DR: In this article, an end-to-end transformer model is proposed for dense video captioning, which employs a self-attention mechanism to enable the use of efficient non-recurrent structure during encoding and leads to performance improvements.