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Works56 from public data
- 1,955
- BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment687
This study redesigns BasicVsr by proposing second-order grid propagation and flow-guided deformable alignment, and shows that by empowering the re-current framework with enhanced propagation and align-ment, one can exploit spatiotemporal information across misaligned video frames more effectively.
- 522
- 370
- GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution305
This work shows that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR) and shows clear improvements in terms of fidelity and texture faithfulness in comparison to existing methods.
- 247
- Video Frame Interpolation Transformer236
A Transformer-based video interpolation framework that allows content-aware aggregation weights and considers long-range dependencies with the self-attention operations and develops a multi-scale frame synthesis scheme to fully realize the potential of Transformers.
- 219
- Investigating Tradeoffs in Real-World Video Super-Resolution185
A stochastic degradation scheme that reduces up to 40% of training time without sacrificing performance is proposed and it is suggested that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference.
- 167
- Towards Real Scene Super-Resolution With Raw Images122
A new pipeline to generate realistic training data by simulating the imaging process of digital cameras is proposed and a dual convolutional neural network is developed to exploit the originally captured radiance information in raw images to recover fine details and clear structures.
- Instant3D: Instant Text-to-3D Generation64
A novel framework for fast text-to-3D generation, dubbed Instant3D, that is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network.
- STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition53
A novel framework STPrivacy is proposed to perform video-level PPAR and introduces vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i.e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective.
- Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising51
A spatial pixel aggregation network is presented and the pixel sampling and averaging strategies for image denoising are learned and the proposed model naturally adapts to image structures and can effectively improve the denoised results.
- NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results43
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results, and gauge the state of the art of video quality enhancement.
- 38
- GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond36
The method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN, and extends to different tasks including image colorization and blind image restoration.
- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
- 35
- On the Generalization of BasicVSR++ to Video Deblurring and Denoising33
The proposed framework achieves compelling performance with great efficiency in various video restoration tasks including video deblurring and denoising and achieves comparable performance to Transformer-based approaches with up to 79% of parameter reduction and 44x speedup.
- Rendering Portraitures from Monocular Camera and Beyond33
This work introduces an automatic system that achieves portrait DoF rendering for monocular cameras using Convolutional Neural Networks and trains a spatially-variant Recursive Neural Network to learn and accelerate this rendering process.
- Exploiting Raw Images for Real-Scene Super-Resolution31
This paper proposes a method to generate more realistic training data by mimicking the imaging process of digital cameras, and develops a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images.
- SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation23
An SMPL-based Transformer framework (SMPLer) is proposed, which incorporates two key ingredients: a decoupled attention operation and an SMPL-based target representation, which allow effective utilization of high-resolution features in the Transformer.
- AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results22
This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results, and employs the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes.
- Super-Resolution Capacitive Touchscreens20
This paper shows how super-resolution techniques, long used in fields such as biology and astronomy, can be applied to capacitive touchscreen data and opens the door to passive tangibles with higher-density fiducials and also recognition of every-day metal objects, such as keys and coins.
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 2026-10-12. 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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