This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality
Assessment (S-UGC VQA) where various excellent solutions are submitted and evaluated …

Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …

Today's deep learning methods focus on how to design the most appropriate objective
functions so that the prediction results of the model can be closest to the ground truth …

We launch EVA-02, a next-generation Transformer-based visual representation pre-trained
to reconstruct strong and robust language-aligned vision features via masked image …

Large-kernel convolutional neural networks (ConvNets) have recently received extensive
research attention but two unresolved and critical issues demand further investigation. 1) …

At the heart of foundation models is the philosophy of" more is different", exemplified by the
astonishing success in computer vision and natural language processing. However, the …

Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

Recent advances in large-scale pretraining have yielded visual foundation models with
strong capabilities. Not only can recent models generalize to arbitrary images for their …

In the past years, YOLO-series models have emerged as the leading approaches in the area
of real-time object detection. Many studies pushed up the baseline to a higher level by …

In this paper, we summarize the 2nd NTIRE challenge on stereo image super-resolution
(SR) with a focus on new solutions and results. The task of the challenge is to super-resolve …