arXiv (All)
open access
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Natural State-Prediction Accuracy can Hide Weak Controlled Responsiveness in VLA Readouts#1128943
- Toward Physically Grounded JEPA World Models for Goal-Conditioned Robotic Planning#926474
- Contrastive World Models#1036858
- Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance#985854
- Revisiting Local Context for Long-Horizon Streaming 3D Reconstruction#780840
Record · ID 1035729
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive