Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks60 from public data
- AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation175
This paper proposes Axial Fusion Transformer UNet (AFTer-UNet), which takes both advantages of convolutional layers’ capability of extracting detailed features and transformers’ strength on long sequence modeling and has fewer parameters and takes less GPU memory to train than the previous transformer-based models.
- 91
- TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation83
A transformer framework for multi-view 3D pose estimation, aiming at directly improving individual 2D predictors by integrating information from different views is introduced, and the concept of epipolar field to encode 3D positional information into the transformer model is proposed.
- An α-Matte Boundary Defocus Model-Based Cascaded Network for Multi-Focus Image Fusion82
Experiments demonstrate that the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.
- 72
- 72
- Real-MFF: A large realistic multi-focus image dataset with ground truth66
A large and realistic multi-focus dataset called Real-MFF is introduced, which contains 710 pairs of source images with corresponding ground truth images, which is generated by light field images, and both the source images and the groundtruth images are realistic.
- 64
- 50
- Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution48
This work proposes a Hybrid Transformer and CNN Attention Network (HTCAN), which utilizes a transformer-based network for single-image enhancement and a CNN-basednetwork for stereo information fusion and employs a multi-patch training strategy and larger window sizes to activate more input pixels for super-resolution.
- 47
- Training Your Sparse Neural Network Better with Any Mask43
This paper demonstrates an alternative opportunity: one can carefully customize the sparse training techniques to deviate from the default dense network training protocols, consisting of introducing ``ghost"neurons and skip connections at the early stage of training, and strategically modifying the initialization as well as labels.
- 39
- 24
- 17
- 16
- 16
- MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers14
An efficient model called MaskINT is introduced, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the edited keyframes, using the structural guidance from intermediate frames.
- 14
- 14
- 14
- 13
- 11
- 11
- 10
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.