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- 1,955
- Non-local recurrent network for image restoration706
A non- local recurrent network (NLRN) is proposed as the first attempt to incorporate non-local operations into a recurrent neural network (RNN) for image restoration and achieves superior results to state-of-the-art methods with much fewer parameters.
- Video Instance Segmentation663
The first time that the image instance segmentation problem is extended to the video domain, and a novel algorithm called MaskTrack R-CNN is proposed for this task, which is simultaneous detection, segmentation and tracking of instances in videos.
- 606
- YouTube-VOS: Sequence-to-Sequence Video Object Segmentation596
This work builds a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS) and proposes a novel sequence-to-sequence network to fully exploit long-term spatial-temporal information in videos for segmentation.
- VRT: A Video Restoration Transformer448
Experimental results on five tasks, including video super-resolution, video deblurring, video denoising, video frame interpolation and space-time video super-resolution, demonstrate that VRT outperforms the state-of-the-art methods by large margins on fourteen benchmark datasets.
- 413
- 360
- Efficient and Explicit Modelling of Image Hierarchies for Image Restoration354
A new network architecture dubbed GRL is proposed to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-Attention, and channel attention enhanced convolution.
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- Pyramid Attention Network for Image Restoration142
A novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid, and is designed to be able to borrow clean signals from their “clean” correspondences at the coarser levels.
- 115
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- NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results106
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results, based on the SIDD benchmark.
- 100
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- Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models63
This study proposes an approach that focuses on the customization of 360-degree panoramas, which inherently possess global geometric properties, using a T2I diffusion model, and exhibits exceptional generalization ability in producing scenes unseen in the fine-tuning dataset.
- NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results54
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single image super- resolution.
- Wide-activated Deep Residual Networks based Restoration for BPG-compressed Images49
A simple pipeline to achieve high-quality image compression under very low bit-rate is investigated, which significantly reduces the quantity loss and remove visual artifacts for compressed images.
- Scale-Wise Convolution for Image Restoration46
It is shown that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance and the proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal.
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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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