Omkar2118/datasets huggingface dataset:
V2VBench

V2VBench is a comprehensive benchmark designed to evaluate video editing methods. It consists of:
- 50 standardized videos across 5 categories, and
- 3 editing prompts per video, encompassing 4 editing tasks: [Huggingface Datasets](https://huggingface.co/datasets/Wenhao-Sun/V2VBench)
- 8 evaluation metrics to assess the quality of edited videos: [Evaluation Metrics](doc/README.md)

For detailed information, please refer to the accompanying paper.

Introducing paper: Diffusion Model-Based Video Editing: A Survey
DiDi
Full name: Distractor Distilled Dataset

DiDi is a distractor-distilled tracking dataset created to address the limitation of low distractor presence in current visual object tracking benchmarks. To enhance the evaluation and analysis of tracking performance amidst distractors, we have semi-automatically distilled several existing benchmarks into the DiDi dataset. The dataset is available for download at this URL: https://go.vicos.si/didi

Introducing paper: A Distractor-Aware Memory for Visual Object Tracking with SAM2
Automatic Nostalgia Detection from Bengali Text

This dataset contains 10089 Bengali comments and its tag( Nostalgic and Non-nostalgic)