27 datasets found

TVQA is a video question answering dataset collected from 6 long-running TV shows from 3 genres. There are 21,793 video clips in total for QA collection, accompanied with...

A video-centric multimodal instruction dataset, composed of thousands of videos associated with detailed descriptions and conversations.

The VidOR dataset is a rich video dataset containing natural videos of daily life.

QVHighlights is a dataset for video highlight detection, which consists of over 10,000 videos annotated with human-written text queries.

MovieChat: From dense token to sparse memory for long video understanding.

Video-Chat2: From dense token to sparse memory for long video understanding.

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 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,...

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...

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...

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.

The Kinetics-400, UCF101, HMDB51, Something-Something V1, and Something-Something V2 datasets are used for evaluating the performance of the Bi-Calibration Networks.