Ishan Misra
Ishan is a research scientist at Facebook. He is currently working on Computer Vision and Machine Learning. His research interest is in reducing the need for supervision in visual learning. He holds a PhD from the Robotics Institute at Carnegie Mellon University and graduated in 2018 when he joined FAIR.
Ishan's Work
Ishan's Publications
June 19, 2020
COMPUTER VISION
ML APPLICATIONS
Self-Supervised Learning of Pretext-Invariant Representations
The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic…
Ishan Misra, Laurens-van der Maaten
June 19, 2020
June 16, 2019
RESEARCH
COMPUTER VISION
Does Object Recognition Work for Everyone?
The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used…
Terrance DeVries, Ishan Misra, Changhan Wang, Laurens van der Maaten
June 16, 2019
June 18, 2018
RESEARCH
Learning by Asking Questions
We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA…
Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten
June 18, 2018
June 14, 2020
RESEARCH
COMPUTER VISION
In Defense of Grid Features for Visual Question Answering
Popularized as ‘bottom-up’ attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA).
Huaizu Jiang, Ishan Misra, Marcus Rohrbach, Erik Learned-Miller, Xinlei Chen
June 14, 2020
May 19, 2020
RESEARCH
COMPUTER VISION
ClusterFit: Improving Generalization of Visual Representations
Pre-training convolutional neural networks with weakly supervised and self-supervised strategies is becoming increasingly popular for several computer vision tasks.
Xueting Yan, Ishan Misra, Abhinav Gupta, Deepti Ghadiyaram, Dhruv Mahajan
May 19, 2020
October 27, 2019
RESEARCH
COMPUTER VISION
Scaling and Benchmarking Self-Supervised Visual Representation Learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, Ishan Misra
October 27, 2019
