arXiv (All)
open access
Image Classifiers are Efficient Self-Supervised Video Representation Learners
We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of relying on heavy 3D architectures or reconstruction-based autoencoders for learning with unlabeled data, we repurpose standard image Vision Transforme
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
- Image Classifiers are Efficient Self-Supervised Video Representation Learners#1136808
- I Have a Stream: Making Self-Supervised Learning Work on Continuous Video#1154593
- Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance#985854
- TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining#1118131
- W2Rep: Learning Visual Representations by Watching the World Change#1130048
Record · ID 1154606
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