arXiv (OAI Expanded)
open access
SGWIB:Sliced Gromov-Wasserstein Information Bottleneck for Video Highlight Detection
Video highlight detection aims to identify temporally important segments that capture the most informative or engaging events in a video. Reliable prediction therefore requires not only discriminative segment representations but also preservation of the temporal relationships among neighboring and d
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
- StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding#620720
- StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding#619041
- VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models#430497
- GleanVID: Complementary Token Selection for Efficient Video Large Language Models#1132243
- VidOmni-Bench: A Benchmark for Fine-Grained Video Understanding via Spatio-Temporal Event Verification across Complexity and Duration#1035920
Record · ID 1083455
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