no code implementations • 29 Dec 2018 • Yash Bhalgat, Meet Shah, Suyash Awate
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved.
no code implementations • CVPR 2019 • Meet Shah, Xinlei Chen, Marcus Rohrbach, Devi Parikh
Despite significant progress in Visual Question Answering over the years, robustness of today's VQA models leave much to be desired.
7 code implementations • CVPR 2019 • Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, Marcus Rohrbach
We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset.
Ranked #3 on Visual Question Answering (VQA) on VizWiz 2018
no code implementations • 23 Jan 2020 • Stefano Pedemonte, Brent Mombourquette, Alexis Goh, Trevor Tsue, Aaron Long, Sadanand Singh, Thomas Paul Matthews, Meet Shah, Jason Su
In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity.
no code implementations • 23 Jan 2020 • Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su
In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images.
no code implementations • ECCV 2020 • Jerry Liu, Shenlong Wang, Wei-Chiu Ma, Meet Shah, Rui Hu, Pranaab Dhawan, Raquel Urtasun
We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames.
no code implementations • 2 Oct 2020 • Meet Shah, Zhiling Huang, Ankit Laddha, Matthew Langford, Blake Barber, Sidney Zhang, Carlos Vallespi-Gonzalez, Raquel Urtasun
In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps.
no code implementations • 13 Apr 2022 • Stefano Pedemonte, Trevor Tsue, Brent Mombourquette, Yen Nhi Truong Vu, Thomas Matthews, Rodrigo Morales Hoil, Meet Shah, Nikita Ghare, Naomi Zingman-Daniels, Susan Holley, Catherine M. Appleton, Jason Su, Richard L. Wahl
This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.