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Works45 from public data
- First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations577
This work collects RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations, and sees clear benefits of using hand pose as a cue for action recognition compared to other data modalities.
- BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis268
This paper proposes a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps captured with minimal restriction on the range of motion, and demonstrates significant improvements in cross-benchmark performance.
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- Video Super-Resolution With Temporal Group Attention199
This work proposes a novel method that can effectively incorporate temporal information in a hierarchical way and achieves favorable performance against state-of-the-art methods on several benchmark datasets.
- 3D Hand Pose Estimation: From Current Achievements to Future Goals194
This paper investigates 11 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction.
- Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation163
A hybrid hand pose estimation method is proposed by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method and to the optimization of the generative method by hierarchical Particle Swarm Optimization (PSO).
- Wavelet-Based Dual-Branch Network for Image Demoiréing158
Although designed for image demoireing, WDNet has been applied to two other low-levelvision tasks, outperforming state-of-the-art image deraining and derain-drop methods on the Rain100h and Raindrop800 data sets, respectively.
- Image Demoireing with Learnable Bandpass Filters126
Experimental results on two public datasets show that the novel multiscale bandpass convolutional neural network (MBCNN) outperforms state-of-the-art methods by a large margin.
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- AIM 2019 Challenge on Image Demoireing: Methods and Results57
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019, and focuses on the proposed solutions and their results.
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- Graph neural networks in vision-language image understanding: a survey48
This survey is the first comprehensive survey that covers image captioning, visual question answering, and image retrieval techniques that focus on using GNNs as the main part of their architecture, and provides a taxonomy of graph types used in 2D image understanding approaches.
- NTIRE 2020 Challenge on Image Demoireing: Methods and Results45
This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020, and examines the current state-of-the-art in image and burst image demoireing problems.
- Improving Dynamic HDR Imaging with Fusion Transformer42
This paper proposes a transformer model for HDR imaging that outperforms the state-of-the-art by large margins on several popular public datasets, and demonstrates that the key component is the HDR transformer module.
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- AIM 2019 Challenge on Image Demoireing: Dataset and Study40
This paper introduces a novel dataset, called LCDMoire, which was created for the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019.
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- NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds25
The holistic priors, including pseudo depth maps and view coverage information, from neural reconstruction are utilized to guide the learning of implicit neural representations of 3D indoor scenes to enable free navigation in a room.
- Wavelet-based network for high dynamic range imaging19
A novel frequency-guided end-to-end deep neural network (FHDRNet) is proposed to conduct HDR fusion in the frequency domain, and Discrete Wavelet Transform (DWT) is used to decompose inputs into different frequency bands.
- SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-trained Siamese Transformers18
A novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements that vary in successive frames, constructed by Siamese transformers, encoders, and decoders called SiamTrans.
- DomainPlus: Cross Transform Domain Learning towards High Dynamic Range Imaging17
This paper proposes a cross-transform domain neural network for efficient HDR imaging, and proposes fronted Discrete Wavelet Transform and Discrete Cosine Transform-based learnable bandpass filters to formulate aCross- transform domain learning block, dubbed DomainPlus Block (DPB) for effective ghosting removal.
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- Non-local degradation modeling for spatially adaptive single image super-resolution12
It is demonstrated that SASR can efficiently estimate degradation representations and handle multiple types of degradation, and leads to state-of-the-art blind SISR performance when compared to alternative approaches.
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- Depth-guided deep filtering network for efficient single image bokeh rendering6
A depth-guided deep filtering network, called DDFN, is proposed in this study, and the focused region detection block is designed to detect the salient areas, and the depth estimated block is introduced to estimate depth maps from full-focus images.
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- Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment3
The method is the first to achieve cross-domain disentanglement for this highly under-constrained problem of extracting disentangled 3D attributes only from 2D image data and demonstrates the framework's ability to transfer pose, shape, and garments in 3D reconstruction on virtual data.
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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 2026-10-16. 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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