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20222024
most citedDAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting

27 citations · 66 across the 14 of their papers we have counts for

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10 papers · 1 filter

cs.CV2024

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection

Hao Tang, Zechao Li, Dong Zhang +2

RGB-Thermal Salient Object Detection aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. Traditional encoder-decoder architectures, whil…

cs.CV2023

NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos

Ziyu Yang, Sucheng Ren, Zongwei Wu +4

Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies. This work aims to take a step forward to understand how humans perc…

cs.CV2023

RIGID: Recurrent GAN Inversion and Editing of Real Face Videos

Yangyang Xu, Shengfeng He, Kwan-Yee K. Wong +1

GAN inversion is indispensable for applying the powerful editability of GAN to real images. However, existing methods invert video frames individually often leading to undesired in…

cs.CV202327 cited

DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting

Huilin Zhu, Jingling Yuan, Xian Zhong +3

Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. However, existing domain adaptation methods tend to focus on inter-da…

cs.CV202314 cited

Disentangling Multi-view Representations Beyond Inductive Bias

Guanzhou Ke, Yang Yu, Guoqing Chao +3

Multi-view (or -modality) representation learning aims to understand the relationships between different view representations. Existing methods disentangle multi-view representatio…

cs.CV2023

Single-View View Synthesis with Self-Rectified Pseudo-Stereo

Yang Zhou, Hanjie Wu, Wenxi Liu +3

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view vie…