10 citations · 22 across the 5 of their papers we have counts for
5 papers
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Hongzuo Xu, Yijie Wang, Juhui Wei +3
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models a…
Dual-Pixel Raindrop Removal
Yizhou Li, Yusuke Monno, Masatoshi Okutomi
Removing raindrops in images has been addressed as a significant task for various computer vision applications. In this paper, we propose the first method using a Dual-Pixel (DP) s…
Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM
Yizhou Li, Yusuke Monno, Masatoshi Okutomi
Single image deraining is typically addressed as residual learning to predict the rain layer from an input rainy image. For this purpose, an encoder-decoder network draws wide atte…
Exploit the potential of Multi-column architecture for Crowd Counting
Junhao Cheng, Zhuojun Chen, XinYu Zhang +2
Crowd counting is an important yet challenging task in computer vision due to serious occlusions, complex background and large scale variations, etc. Multi-column architecture is w…
Deep Density-aware Count Regressor
Zhuojun Chen, Junhao Cheng, Yuchen Yuan +3
We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency. We leverage multilev…