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20212023
most citedTowards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors

2 citations · 6 across the 5 of their papers we have counts for

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cs.CV2023★ 1 cited

Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery

Zichen Tian, Zhaozheng Chen, Qianru Sun

We explore the "hidden" ability of large-scale pre-trained image generation models, such as Stable Diffusion and Imagen, in non-visible light domains, taking Synthetic Aperture Rad…

cs.CV2022★ 1 cited

Domain Adaptive Scene Text Detection via Subcategorization

Zichen Tian, Chuhui Xue, Jingyi Zhang +1

Most existing scene text detectors require large-scale training data which cannot scale well due to two major factors: 1) scene text images often have domain-specific distributions…

cs.CV2022★ 2 cited

Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors

Gongjie Zhang, Zhipeng Luo, Zichen Tian +3

Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transfo…

cs.CV2022

Fourier Document Restoration for Robust Document Dewarping and Recognition

Chuhui Xue, Zichen Tian, Fangneng Zhan +2

State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distort…

cs.CV2021★ 2 cited

Spectral Unsupervised Domain Adaptation for Visual Recognition

Jingyi Zhang, Jiaxing Huang, Zichen Tian +1

Though unsupervised domain adaptation (UDA) has achieved very impressive progress recently, it remains a great challenge due to missing target annotations and the rich discrepancy…