243 citations · 251 across the 5 of their papers we have counts for
7 papers
EDTER: Edge Detection with Transformer
Mengyang Pu, Yaping Huang, Yuming Liu +2
Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually…
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
Mengyang Pu, Yaping Huang, Qingji Guan +1
As a fundamental building block in computer vision, edges can be categorised into four types according to the discontinuity in surface-Reflectance, Illumination, surface-Normal or…
Transform consistency for learning with noisy labels
Rumeng Yi, Yaping Huang
It is crucial to distinguish mislabeled samples for dealing with noisy labels. Previous methods such as Coteaching and JoCoR introduce two different networks to select clean sample…
Classification-driven Single Image Dehazing
Yanting Pei, Yaping Huang, Xingyuan Zhang
Most existing dehazing algorithms often use hand-crafted features or Convolutional Neural Networks (CNN)-based methods to generate clear images using pixel-level Mean Square Error…
Does Haze Removal Help CNN-based Image Classification?
Yanting Pei, Yaping Huang, Qi Zou +2
Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to pro…
Effects of Image Degradations to CNN-based Image Classification
Yanting Pei, Yaping Huang, Qi Zou +3
Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep-learning neural networks, especially the Convolutional…