Video-based Generative Performance Benchmarking (Contextual Understanding)
11 papers with code • 1 benchmarks • 1 datasets
The benchmark evaluates a generative Video Conversational Model with respect to Contextual Understanding.
We curate a test set based on the ActivityNet-200 dataset, featuring videos with rich, dense descriptive captions and associated question-answer pairs from human annotations. We develop an evaluation pipeline using the GPT-3.5 model that assigns a relative score to the generated predictions on a scale of 1-5.
Benchmarks
These leaderboards are used to track progress in Video-based Generative Performance Benchmarking (Contextual Understanding)
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VideoInstruct
Most implemented papers
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
Vision-CAIR/MiniGPT4-video
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This paper introduces MiniGPT4-Video, a multimodal Large Language Model (LLM) designed specifically for video understanding.
