most citedTowards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks

11 citations · 15 across the 3 of their papers we have counts for

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cs.CV20242 cited

HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention

Xiaolong Tang, Meina Kan, Shiguang Shan +3

Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm, which predicts the future trajectory…

cs.CV20231 cited

Patch Is Not All You Need

Changzhen Li, Jie Zhang, Yang Wei +3

Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks. However, their inherent reliance on sequential input e…

cs.CV2023

Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis

Yuxiang Wei, Zhilong Ji, Xiaohe Wu +3

Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can…

cs.CV20231 cited

TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition

Tianlun Zheng, Zhineng Chen, Jinfeng Bai +2

Text irregularities pose significant challenges to scene text recognizers. Thin-Plate Spline (TPS)-based rectification is widely regarded as an effective means to deal with them. C…

cs.CV20231 cited

CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model

Zhongqi Wang, Jie Zhang, Zhilong Ji +2

With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese land…

cs.CV20224 cited

When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition

Bohan Li, Ye Yuan, Dingkang Liang +5

Recently, most handwritten mathematical expression recognition (HMER) methods adopt the encoder-decoder networks, which directly predict the markup sequences from formula images wi…