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- Yuncai LiuSuggested from co-authorship
- Masatoshi OkutomiSuggested from co-authorship
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Works168 from public data
- MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing902
MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency, and proposes a mask-guided mutual self-attention strategy.
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- Physics-based Noise Modeling for Extreme Low-light Photography188
Extensive experiments on multiple low-light denoising datasets show that a deep neural network trained with the proposed noise formation model can reach surprisingly-high accuracy, opening a new door to real-world extreme low- light photography.
- Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring178
This work adopts residual dense blocks into RNN cells into RNN cells, and proposes a global spatio-temporal attention module to fuse the hierarchical features from past and future frames to help better deblur the current frame.
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- SSF-CNN: Spatial and Spectral Fusion with CNN for Hyperspectral Image Super-Resolution121
Experimental results validate that the proposed spatial and spectral fusion CNNs outperforms the state-of-the-art methods and baseline CNN architectures in both quantitative values and visual qualities.
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- ASPnP: An Accurate and Scalable Solution to the Perspective-n-Point Problem81
The main idea is to estimate the orientation and position parameters by directly minimizing a properly defined algebraic error by using a novel quaternion representation of the rotation, which is immune to any parametrization degeneracy.
- Convolutional demosaicing network for joint chromatic and polarimetric imagery78
The experimental results show that the proposed methods are capable of faithfully recovering full 12-channel chromatic and polarimetric information for each pixel from a single mosaic image in terms of quantitative measures and visual quality.
- Multi-Level and Multi-Scale Spatial and Spectral Fusion CNN for Hyperspectral Image Super-Resolution73
- Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding72
A unified framework to disentangle the style factor and the fog factor separately, and then the dual-factor from images in different domains is devised, which achieves the state-of-the-art performance on three benchmarks and shows generalization ability in rainy and snowy scenes.
- Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification69
This paper provides a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies and compares the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline.
- Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes67
The first learning-based model (JCD) for RSCD is presented, which adopts bi-directional warping streams for displacement compensation, while also preserving the non-warped deblurring stream for details restoration.
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- Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery63
An efficient convolutional neural network (CNN) based method, which can jointly select the optimal CSS from a candidate dataset and learn a mapping to recover HSI from a single RGB image captured with this algorithmically selected camera.
- Blur Interpolation Transformer for Real-World Motion from Blur62
A blur interpolation transformer (BiT) is proposed to effectively unravel the underlying temporal correlation encoded in blur to generate effective features for time-varying motion rendering and a hybrid camera system is designed to collect the first real-world dataset of one-to-many blur-sharp video pairs.
- Real-World Video Deblurring: A Benchmark Dataset and an Efficient Recurrent Neural Network61
A novel dataset (BSD) is contributed to the community, by collecting paired blurry/sharp video clips using a co-axis beam splitter acquisition system and a global spatio-temporal attention module is proposed to fuse the effective hierarchical features from past and future frames to better deblur the current frame.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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