VideoChat3-Academic2M

VideoChat3-Academic2M VideoChat3-Academic2M is the academic video instruction data used by VideoChat3. It re-annotates public academic video datasets for video captioning, video question answering, and fine-grained motion understanding. The dataset follows an evidence-grounded annotation enhancement pipeline. Short answers, option-only labels, and concise captions are rewritten into richer instruction-following responses that mention visible objects, actions, scenes, temporal cues, and supporting evidence. A consistency filtering stage is then used to keep rewritten annotations aligned with the original academic labels. This repository provides JSONL annotation files. The original videos are not duplicated here; users should resolve videos from the original dataset paths listed below. 📄 Paper · 🌐 Homepage · 💻 GitHub · 🤗 Paper Page Data Sources VideoChat3-Academic2M aggregates academic caption/QA sources including LLaVA-Video, Spoken-MIT, Vript, StarQA, Sports-QA, and Perception-Test. | Source dataset | Original video dataset path | | --- | --- | | LLaVA-Video | https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K | | S-MiT | https://arxiv.org/pdf/2105.04489 (Re-uploaded by us) | | Vript | https://huggingface.co/datasets/Mutonix/Vript | | StarQA |https://github.com/csbobby/STAR_Benchmark | | Sports-QA | https://huggingface.co/datasets/HopLeeTop/Sports-QA | | Perception-Test | https://github.com/google-deepmind/perception_test | The `acadmic2M.json` file provides the mapping between dataset annotations and video sources. You can refer to it when organizing the dataset structure. Citation If you use this data, please cite VideoChat3 and the original video datasets used by the annotations.