SOURCE-LINKED INTELLIGENCE
High-Fidelity Video Quality Assessment with VQA-Specific Saliency
No-reference video quality assessment (NR VQA) has recently seen promising progress with deep learning. However, video data is inherently large, and processing them with deep models incurs high computational cost. This challenge is particularly acute in VQA, where preserving original-resolution cues and dense temporal information is critical for accuracy. Existing efficiency-driven preprocessing strategies, such as fragmenting, reduce computation but alter the input data distribution, limiting effective reuse of pretrained video foundation models (ViFMs). To address these challenges, we propos
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T10:23:05.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.