47 citations · 82 across the 4 of their papers we have counts for
7 papers
WegFormer: Transformers for Weakly Supervised Semantic Segmentation
Chunmeng Liu, Enze Xie, Wenjia Wang +3
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insuffici…
Segmenting Transparent Object in the Wild with Transformer
Enze Xie, Wenjia Wang, Wenhai Wang +4
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation data…
Scene Text Image Super-Resolution in the Wild
Wenjia Wang, Enze Xie, Xuebo Liu +4
Low-resolution text images are often seen in natural scenes such as documents captured by mobile phones. Recognizing low-resolution text images is challenging because they lose det…
Segmenting Transparent Objects in the Wild
Enze Xie, Wenjia Wang, Wenhai Wang +3
Transparent objects such as windows and bottles made by glass widely exist in the real world. Segmenting transparent objects is challenging because these objects have diverse appea…
TextSR: Content-Aware Text Super-Resolution Guided by Recognition
Wenjia Wang, Enze Xie, Peize Sun +4
Scene text recognition has witnessed rapid development with the advance of convolutional neural networks. Nonetheless, most of the previous methods may not work well in recognizing…
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
Wenhai Wang, Enze Xie, Xiaoge Song +5
Scene text detection, an important step of scene text reading systems, has witnessed rapid development with convolutional neural networks. Nonetheless, two main challenges still ex…