98 citations · 127 across the 10 of their papers we have counts for
9 papers · 1 filter
PANDORA: Polarization-Aided Neural Decomposition Of Radiance
Akshat Dave, Yongyi Zhao, Ashok Veeraraghavan
Reconstructing an object's geometry and appearance from multiple images, also known as inverse rendering, is a fundamental problem in computer graphics and vision. Inverse renderin…
CodedStereo: Learned Phase Masks for Large Depth-of-field Stereo
Shiyu Tan, Yicheng Wu, Shoou-I Yu +1
Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these vari…
Fine-grained Classification using Heterogeneous Web Data and Auxiliary Categories
Li Niu, Ashok Veeraraghavan, Ashu Sabharwal
Fine-grained classification remains a very challenging problem, because of the absence of well-labeled training data caused by the high cost of annotating a large number of fine-gr…
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning
Wanjia Liu, Huaijin Chen, Rishab Goel +3
Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks. In such framework, besides…
Learning from Noisy Web Data with Category-level Supervision
Li Niu, Qingtao Tang, Ashok Veeraraghavan +1
As tons of photos are being uploaded to public websites (e.g., Flickr, Bing, and Google) every day, learning from web data has become an increasingly popular research direction bec…
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Huaijin Chen, Jinwei Gu, Orazio Gallo +3
Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image forma…