arXiv (All)
open access
A Controlled Study of Self-Supervised Image and Video Pretraining under Limited Resources
Visual foundation models are a cornerstone of image and video understanding but typically require large amounts of data and computation. The current scale required for pretraining visual foundation models may be unsustainable or unnecessary, and significant benefits arise when effective models can b
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
- Towards Vision-Language Geo-Foundation Model: A Survey#972363
- Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce#1070294
- Advancing Video-Text Pretraining with Multi-View Captions#1129691
- How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks#189297
- VI3: Grounding Pretrained 3D Foundation Models with Inertial Cues#926705
Record · ID 1105141
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