reasoning-9
Video-based Generative Performance Benchmarking (Correctness of Information)
3260 papers • 126 benchmarks • 313 datasets
The benchmark evaluates a generative Video Conversational Model with respect to Correctness of Information. 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.
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Most implemented papers
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Hongsheng Li, Conghui He, Pan Lu, Renrui Zhang, Peng Gao, W. Zhang, Xiangyu Yue, Y. Qiao, Jiaming Han, Aojun Zhou, Ziyi Lin, Shijie Geng•Thu Apr 27 2023
This work augments LLaMA-Adapter by unlocking more learnable parameters and proposes an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset.