2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2022
Exploiting Instance-based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-adaptive Action Detection
Yifan Lu, Gurkirt Singh, Suman Saha +1
We propose a novel domain adaptive action detection approach and a new adaptation protocol that leverages the recent advancements in image-level unsupervised domain adaptation (UDA…
cs.LG2021★ 2 cited
Should Graph Neural Networks Use Features, Edges, Or Both?
Lukas Faber, Yifan Lu, Roger Wattenhofer
Graph Neural Networks (GNNs) are the first choice for learning algorithms on graph data. GNNs promise to integrate (i) node features as well as (ii) edge information in an end-to-e…
cs.CV2019
DeepSquare: Boosting the Learning Power of Deep Convolutional Neural Networks with Elementwise Square Operators
Sheng Chen, Xu Wang, Chao Chen +3
Modern neural network modules which can significantly enhance the learning power usually add too much computational complexity to the original neural networks. In this paper, we pu…