Jason J. Corso

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.

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.

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.

TL;DR: In this paper, a Bayesian formulation for incorporating soft model assignments into the calculation of affinities is presented. And the resulting soft model assignment is integrated into the multilevel segmentation by weighted aggregation algorithm, and applied to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes.

TL;DR: The results show the approach outperforms SVM, CRF and CCA baselines in predicting Subject-Verb-Object triplet and natural sentence generation, and is better than CCA in video retrieval and language retrieval tasks.