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20152025
most citedLocal Patch Network with Global Attention for Infrared Small Target Detection

81 citations · 730 across the 112 of their papers we have counts for

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Showing 2023 · cs.CVShow all

20 papers · 2 filters

cs.CV2023

MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces

Zhicun Yin, Ming Liu, Xiaoming Li +3

Due to their highly structured characteristics, faces are easier to recover than natural scenes for blind image super-resolution. Therefore, we can extract the degradation represen…

cs.CV2023★ 11 cited

Aggregating Nearest Sharp Features via Hybrid Transformers for Video Deblurring

Wei Shang, Dongwei Ren, Yi Yang +1

Video deblurring methods, aiming at recovering consecutive sharp frames from a given blurry video, usually assume that the input video suffers from consecutively blurry frames. How…

cs.CV2023★ 1 cited

Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration

Yuanshuo Cheng, Mingwen Shao, Yecong Wan +3

Existing Video Restoration (VR) methods always necessitate the individual deployment of models for each adverse weather to remove diverse adverse weather degradations, lacking the…

cs.CV2023

Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Minheng Ni, Yabo Zhang, Kailai Feng +3

Zero-shot referring image segmentation is a challenging task because it aims to find an instance segmentation mask based on the given referring descriptions, without training on th…

cs.CV2023★ 3 cited

VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization

Mingshuai Yao, Yabo Zhang, Xianhui Lin +2

Few-shot font generation is challenging, as it needs to capture the fine-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which…

cs.CV2023★ 2 cited

Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning

Chun-Mei Feng, Kai Yu, Yong Liu +2

Benefiting from prompt tuning, recent years have witnessed the promising performance of pre-trained vision-language models, e.g., CLIP, on versatile downstream tasks. In this paper…