98 citations · 128 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
Deep -Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Junru Wu, Yue Wang, Zhenyu Wu +3
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and…
Signal Processing Based Pile-up Compensation for Gated Single-Photon Avalanche Diodes
Adithya K. Pediredla, Aswin C. Sankaranarayanan, Mauro Buttafava +2
Single-photon avalanche diode (SPAD) based transient imaging suffers from an aberration called pile-up. When multiple photons arrive within a single repetition period of the illumi…
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…
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan +1
Phase retrieval algorithms have become an important component in many modern computational imaging systems. For instance, in the context of ptychography and speckle correlation ima…