arXiv (All)
open access
TempCore: Are Video QA Benchmarks Temporally Grounded?
Vision-language models (VLMs) can ingest only a limited number of video frames, making frame selection a practical necessity. But do current Video QA benchmarks genuinely require temporal frame selection, or can most questions be answered regardless of which frames are shown? We introduce Frame Sele
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
- VidOmni-Bench: A Benchmark for Fine-Grained Video Understanding via Spatio-Temporal Event Verification across Complexity and Duration#1035920
- GleanVID: Complementary Token Selection for Efficient Video Large Language Models#1132243
- Official Review · ICLR 2025#787098
- TRACE: Transparent Retrieval for Abstract Concept Evaluation#1062070
- AgentVidBench: A Multi-Hop Video Question Answering Benchmark for Evaluating MLLM Agents#1035790
Record · ID 1049665
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