SOURCE-LINKED INTELLIGENCE
S3VD: Semantic-Guidance Spatio-Temporal Scanning for Video Deraining
Heavy rainfall severely degrades outdoor videos by corrupting high-frequency details and introducing motion blur, critically undermining the reliability of visual tasks. Recently, State Space Models (SSMs), particularly Mamba, have emerged as efficient alternatives for vision tasks with their linear complexity and ability to model long-range dependencies. However, when confronted with the poor visual representations in rainy videos, Mamba still faces difficulties in preserving the integrity of 2D spatial semantics and modeling 3D spatio-temporal correlations. To break these limitations, we int
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T04:59:46.000Z
First collected: 2026-09-23T14:01:59.594Z. This is not the publication date.