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20182022
most citedMulti-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

25 citations · 82 across the 17 of their papers we have counts for

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26 papers · 1 filter

cs.CV20223 cited

Few-shot Medical Image Segmentation with Cycle-resemblance Attention

Hao Ding, Changchang Sun, Hao Tang +2

Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing…

cs.CV20222 cited

Deep Unsupervised Key Frame Extraction for Efficient Video Classification

Hao Tang, Lei Ding, Songsong Wu +3

Video processing and analysis have become an urgent task since a huge amount of videos (e.g., Youtube, Hulu) are uploaded online every day. The extraction of representative key fra…

cs.CV20221 cited

Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis

Hao Tang, Ling Shao, Philip H. S. Torr +1

We present a novel bipartite graph reasoning Generative Adversarial Network (BiGraphGAN) for two challenging tasks: person pose and facial image synthesis. The proposed graph gener…

cs.CV2022

Cross-View Panorama Image Synthesis

Songsong Wu, Hao Tang, Xiao-Yuan Jing +4

In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap betwee…

cs.CV20222 cited

Local and Global GANs with Semantic-Aware Upsampling for Image Generation

Hao Tang, Ling Shao, Philip H. S. Torr +1

In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small o…

cs.CV2022

Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

Guanglei Yang, Enrico Fini, Dan Xu +5

Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved perform…