50 datasets found

Video-LLaVA: Learning united visual representation by alignment before projection.

Video-Chat: Chat-centric video understanding.

Video-LLaMA: An instruction-tuned audio-visual language model for video understanding.

Video-ChatGPT: Towards detailed video understanding via large vision and language models.

The dataset used in the paper is ActivityNet, MSR-VTT, and MSVD. The authors used these datasets for text-to-video retrieval tasks.

The High-Quality Fall Simulation Dataset (HQFSD) is a challenging dataset for human fall detection, including multi-person scenarios, changing lighting, occlusion, and...

The MSRVTT is a large-scale dataset for video captioning. It contains 10k video clips and each video clip is accompanied with 20 human-edited English sentence descriptions,...

ATLAS Dione is a dataset for RAS video understanding, providing video data of ten surgeons performing six different surgical tasks on the daVinci Surgical System (dVSS...

The TGIF-QA dataset consists of 165165 QA pairs chosen from 71741 animated GIFs. To evaluate the spatiotemporal reasoning ability at the video level, TGIF-QA dataset designs...

A video-LLaMA model for video understanding, comprising 100k videos with detailed captions.

A video-based instruction dataset for video understanding, comprising 100k videos with detailed captions.

A large multi-modal instruction-following dataset for video understanding, comprising 37k conversation pairs, 26k complex reasoning QA pairs and 10k detail description...

A dataset for video understanding, containing images and videos.

A dataset for video understanding, containing images and videos.

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in...

Visual Semantic Role Labeling for Video Understanding.

Fine-grained adaptation of the popular CLIP model across multiple datasets.

The authors used several video benchmarks, including Kinetics-400 and Something-Something V2, to evaluate their proposed motion-guided masking algorithm.

The UniFormer dataset is a video understanding dataset used for learning rich and multi-scale spatiotemporal semantics from high-dimensional videos.

The Kinetics-400 and Kinetics-600 datasets are video understanding datasets used for learning rich and multi-scale spatiotemporal semantics from high-dimensional videos.