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20162025
most citedEfficient and Explicit Modelling of Image Hierarchies for Image Restoration

18 citations · 23 across the 9 of their papers we have counts for

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cs.CV2025

HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking

Ruixiang Chen, Guolei Sun, Yawei Li +2

This paper presents enhancements to the SAM2 framework for video object tracking task, addressing challenges such as occlusions, background clutter, and target reappearance. We int…

cs.CV2025

The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report

Bin Ren, Hang Guo, Lei Sun +143

This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR). The challenge aimed to advance the development of deep mode…

cs.CV20241 cited

Hierarchical Information Flow for Generalized Efficient Image Restoration

Yawei Li, Bin Ren, Jingyun Liang +5

While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To…

cs.CV20241 cited

Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning

Bin Ren, Guofeng Mei, Danda Pani Paudel +6

Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cl…

cs.CV2024

Empowering Image Recovery_ A Multi-Attention Approach

Juan Wen, Yawei Li, Chao Zhang +3

We propose Diverse Restormer (DART), a novel image restoration method that effectively integrates information from various sources (long sequences, local and global regions, featur…

cs.CV20241 cited

Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary

Leheng Zhang, Yawei Li, Xingyu Zhou +2

Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks…