13 citations · 36 across the 4 of their papers we have counts for
6 papers · 1 filter
CNeRV: Content-adaptive Neural Representation for Visual Data
Hao Chen, Matt Gwilliam, Bo He +2
Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have u…
NeRV: Neural Representations for Videos
Hao Chen, Bo He, Hanyu Wang +3
We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we rep…
The Lottery Ticket Hypothesis for Object Recognition
Sharath Girish, Shishira R. Maiya, Kamal Gupta +3
Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep network…
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet
Hao Chen, Abhinav Shrivastava
Ensemble learning is a general technique to improve accuracy in machine learning. However, the heavy computation of a ConvNets ensemble limits its usage in deep learning. In this p…
Progressive Object Transfer Detection
Hao Chen, Yali Wang, Guoyou Wang +2
Recent development of object detection mainly depends on deep learning with large-scale benchmarks. However, collecting such fully-annotated data is often difficult or expensive fo…
LSTD: A Low-Shot Transfer Detector for Object Detection
Hao Chen, Yali Wang, Guoyou Wang +1
Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a targe…