activity
20192022
most citedAIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

20 citations · 47 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20225 cited

An Investigation into Whitening Loss for Self-supervised Learning

Xi Weng, Lei Huang, Lei Zhao +3

A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of…

cs.CV2022

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li, Kai Zhang, Radu Timofte +108

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…

cs.CV20223 cited

Energy-based Latent Aligner for Incremental Learning

K J Joseph, Salman Khan, Fahad Shahbaz Khan +2

Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks…

cs.CV20222 cited

Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer

Omkar Thawakar, Sanath Narayan, Jiale Cao +6

State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features dur…

cs.CV202020 cited

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Pengxu Wei, Hannan Lu, Radu Timofte +68

This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…

cs.CV201917 cited

Learning Digital Camera Pipeline for Extreme Low-Light Imaging

Syed Waqas Zamir, Aditya Arora, Salman Khan +2

In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio…