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20212026
most citedStarVQA: Space-Time Attention for Video Quality Assessment

2 citations · 3 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Smooth bounding box for object detection

Chao Zhou, Shenghua He, Fengchuang Xing +2

Deep learning-based object detection models are highly dependent on the quality of annotated training data. However, large-scale datasets often contain inevitable label noise, with…

cs.CV2024★ 1 cited

Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception

Jiancong Feng, Yuan-Gen Wang, Mingjie Li +1

Self-similarity techniques are booming in blind super-resolution (SR) due to accurate estimation of the degradation types involved in low-resolution images. However, high-dimension…

cs.CV2024

CLIPVQA:Video Quality Assessment via CLIP

Fengchuang Xing, Mingjie Li, Yuan-Gen Wang +2

In learning vision-language representations from web-scale data, the contrastive language-image pre-training (CLIP) mechanism has demonstrated a remarkable performance in many visi…

cs.CV2024

FRRffusion: Unveiling Authenticity with Diffusion-Based Face Retouching Reversal

Fengchuang Xing, Xiaowen Shi, Yuan-Gen Wang +1

Unveiling the real appearance of retouched faces to prevent malicious users from deceptive advertising and economic fraud has been an increasing concern in the era of digital econo…

cs.CV2023

StarVQA+: Co-training Space-Time Attention for Video Quality Assessment

Fengchuang Xing, Yuan-Gen Wang, Weixuan Tang +2

Self-attention based Transformer has achieved great success in many computer vision tasks. However, its application to video quality assessment (VQA) has not been satisfactory so f…

cs.CV2021★ 2 cited

StarVQA: Space-Time Attention for Video Quality Assessment

Fengchuang Xing, Yuan-Gen Wang, Hanpin Wang +2

The attention mechanism is blooming in computer vision nowadays. However, its application to video quality assessment (VQA) has not been reported. Evaluating the quality of in-the-…