activity
20202026
most citedBANet: Blur-aware Attention Networks for Dynamic Scene Deblurring

127 citations · 140 across the 9 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

Reference-Efficient Multimodal Bitstream-Corrupted Video Recovery via Depth-Conditioned Adaptation

Tianyi Liu, Jiaojiao Wang, Ye Lu +4

Recovering visually faithful content from damaged video bitstreams is important for reliable multimedia communication and storage. Existing methods exploit local residuals and non-…

cs.CV2026

FLM: Frequency-Aware Language Models for Generative Image Compression

Jiarun Chen, Kejun Wu, Li Li +3

Generative models have significantly improved the performance ceiling of image lossy compression at low bitrates by exploiting learned priors. However, the generated textures and s…

cs.CV2022★ 1 cited

Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning

Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen +5

Learning-based pre-simulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circu…

cs.CV2021★ 7 cited

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

Bing Li, Yuanlue Zhu, Yitong Wang +3

In this paper, we propose a novel framework to translate a portrait photo-face into an anime appearance. Our aim is to synthesize anime-faces which are style-consistent with a give…

cs.CV2021★ 127 cited

BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring

Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin +2

Image motion blur results from a combination of object motions and camera shakes, and such blurring effect is generally directional and non-uniform. Previous research attempted to…

cs.CV2020★ 2 cited

DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition

Hao-Chiang Shao, Kang-Yu Liu, Chia-Wen Lin +1

The performance of a convolutional neural network (CNN) based face recognition model largely relies on the richness of labelled training data. Collecting a training set with large…