arXiv (All)
open access
Beyond Similarity: Foundation Models as an Efficient Backbone for Training-Free Composed Video Retrieval
Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gallery scale, this creates a fundamental tension: compact embeddings enable efficient, reusable search but can miss the transient actions, state change
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
- CurvSpec: Adaptive Multi-Curvature Learning for Partial Relevant Video Retrieval#1132016
- Hypergraph-Regularized Gramian Volumes for Multimodal Retrieval#1109350
- LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval#160885
- GleanVID: Complementary Token Selection for Efficient Video Large Language Models#1132243
- Allocate Before You Embed: Adaptive Visual Input Allocation for Video Embeddings#822205
Record · ID 996806
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