SVFSearch: New Benchmark for Multimodal Short-Video Frame Search in Gaming
SVFSearch has been launched by researchers as the inaugural open benchmark for short-video frame searches within the Chinese gaming sector. This benchmark tackles the complexities of employing multimodal large language models (LLMs) as foundational agents that interpret ambiguous paused frames from short videos, necessitating knowledge that is vertical, niche, and rapidly changing to respond to inquiries. It includes 5,000 multiple-choice test items and 4,198 supplementary training examples, all focused on authentic game scenes from short video clips. To facilitate consistent and reproducible assessments, it offers a static offline retrieval setup featuring a game-domain text corpus, a topic-related image gallery, and interfaces for text, image, and multimodal retrieval, eliminating dependence on uncontrolled web search APIs. This research is documented in arXiv:2605.17946.
Key facts
- SVFSearch is the first open benchmark for short-video frame search in the Chinese gaming domain.
- It contains 5,000 four-choice test examples and 4,198 auxiliary training examples.
- Each example is centered on a paused game scene from a real short-video clip.
- The benchmark provides a frozen offline retrieval environment with a game-domain text corpus and a topic-linked image gallery.
- It includes text, image, and multimodal retrieval interfaces.
- The benchmark avoids reliance on uncontrolled web search APIs.
- It evaluates multimodal LLMs as agent backbones for understanding ambiguous paused frames.
- The work is published on arXiv with ID 2605.17946.
Entities
Institutions
- arXiv