reasoning-1
Video-based Generative Performance Benchmarking (Temporal Understanding)
3260 papers • 126 benchmarks • 313 datasets
The benchmark evaluates a generative Video Conversational Model with respect to Temporal 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.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in video-based-generative-performance-benchmarking-temporal-understanding-1
Trend
Dataset
Best Model
Actions
VideoInstruct
VideoInstruct
Libraries
iUse these libraries to find video-based-generative-performance-benchmarking-temporal-understanding-1 models and implementations
Datasets
Subtasks
No subtasks available.
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.