Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- C.‐C. Jay KuoSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works310 from public data
- Enhanced Deep Residual Networks for Single Image Super-Resolution7,719
This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks7,565
This work presents a highly accurate single-image superresolution (SR) method using a very deep convolutional network inspired by VGG-net used for ImageNet classification and uses extremely high learning rates enabled by adjustable gradient clipping.
- Deeply-Recursive Convolutional Network for Image Super-Resolution3,021
This work proposes an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN) with two extensions: recursive-supervision and skip-connection, which outperforms previous methods by a large margin.
- Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring2,588
This work proposes a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources and presents a new large-scale dataset that provides pairs of realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera.
- 1,955
- 1,264
- 646
- Part-Aligned Bilinear Representations for Person Re-identification557
A novel network that learns a part-aligned representation for person re-identification that handles the body part misalignment problem, that is, body parts are misaligned across human detections due to pose/viewpoint change and unreliable detection.
- Reweighted Random Walks for Graph Matching545
This work proposes a robust graph matching algorithm against outliers and deformation by simulating random walks with reweighting jumps enforcing the matching constraints on the association graph and achieves noise-robust graph matching by iteratively updating and exploiting the confidences of candidate correspondences.
- I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image518
The proposed I2L-MeshNet predicts the per-lixel likelihood on 1D heatmaps for each mesh vertex coordinate instead of directly regressing the parameters, and preserves the spatial relationship in the input image and models the prediction uncertainty.
- Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose488
Pose2Mesh is a novel graph convolutional neural network (GraphCNN)-based system that estimates the 3D coordinates of human mesh vertices directly from the 2D human pose, which avoids the representation issues, while fully exploiting the mesh topology using a GraphCNN in a coarse-to-fine manner.
- V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map473
This model is designed as a 3D CNN that provides accurate estimates while running in real-time and outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based3D hand pose estimation challenge.
- 445
- InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image427
A large-scale dataset, InterHand2.6M, and a baseline network, InterNet, for 3D interacting hand pose estimation from a single RGB image, which serves as a strong baseline for this new dataset.
- Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation From a Single RGB Image382
This work firstly proposes a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image, which achieves comparable results with the state-of-the-art 3D single- person pose estimation models without any groundtruth information and significantly outperforms previous 3DMulti-Person pose estimation methods on publicly available datasets.
- Channel Attention Is All You Need for Video Frame Interpolation381
A simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component, and achieves outstanding performance compared to the existing models with a component for optical flow computation.
- 314
- Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video303
This work presents a temporally consistent mesh recovery system (TCMR) that effectively focuses on the past and future frames’ temporal information without being dominated by the current static feature of the current frame.
- Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images281
This paper regularizes the optimization process of 3D Gaussian splatting with the adjusted depth and an additional unsupervised smooth constraint, thereby effectively reducing the occurrence of floating artifacts.
- Deep vessel segmentation by learning graphical connectivity253
Comparisons are performed on four retinal image datasets and a coronary artery X-ray angiography dataset, showing that the proposed method outperforms or is on par with current state-of-the-art methods in terms of the average precision and the area under the receiver operating characteristic curve.
- 239
- AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network224
This work develops AP-BSN, a state-of-the-art self-supervised denoising method for real-world sRGB images, and proposes random-replacing refinement, which significantly improves the performance of the AP- BSN without any additional parameters.
- 217
- 208
- 196
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.