video-annotation
video-detail-annotation
Video Detail Annotation — Contra Labs
A human-annotated evaluation dataset comparing AI-generated product videos across three leading video generation models: Google Veo 3.1, Adobe Firefly Video, and Grok Imagine (xAI).
Annotations were collected by professional video editors sourced from the Contra network, a platform connecting top independent professionals across creative and technical fields. Annotators reviewed each video and left timestamped, dimension-specific comments on… See the full description on the dataset page: https://huggingface.co/datasets/contralabs/video-detail-annotation.kwext-bilibili-video-title-annotations
KwExt Bilibili Video Title Annotations
This dataset is a model-assisted annotation set for the KwExt keyword
extraction project. The current snapshot contains 5,000 Chinese Bilibili
video titles from annotation stages video_title_zh_001 through
video_title_zh_005, with 1,000 records in each stage.
The release is intended for early experiments with:
extracting title-grounded keywords and ranking their importance;
broad semantic tags for retrieval and RAG metadata;
dense tag… See the full description on the dataset page: https://huggingface.co/datasets/Himpq/kwext-bilibili-video-title-annotations.4k-video-annotations
4K Video Annotations — Shot Segmentation and Camera Motion
This dataset contains 12 frame-accurate shot clips segmented from five short cinematic video sequences. Every clip is paired with a detailed, manually reviewed annotation covering visible content, subject actions, shot scale, camera angle, camera movement, movement direction, stabilization, composition, lighting, color, pacing, transitions, timecodes, and technical properties.
The footage depicts a tense nighttime… See the full description on the dataset page: https://huggingface.co/datasets/LianeMarilin/4k-video-annotations.multimodal-video-annotation-samples
Video Annotation Samples – SuperviseLab
SuperviseLab provides professional video annotation data for training multimodal AI models. This public sample dataset demonstrates our annotation methodology and output quality across diverse video content categories.
Note: All visual assets in this dataset have been abstracted (pixelated mosaic) to protect source privacy. Uploader identity, original titles, and all identifiable metadata have been removed. This is a demonstration dataset… See the full description on the dataset page: https://huggingface.co/datasets/superviselab/multimodal-video-annotation-samples.video_annotation_pipeline
👁️ Semi-Automatic Video Annotation Pipeline
📝 Description
Video-ChatGPT introduces the VideoInstruct100K dataset, which employs a semi-automatic annotation pipeline to generate 75K instruction-tuning QA pairs. To address the limitations of this annotation process, we present VCG+112K dataset developed through an improved annotation pipeline. Our approach improves the accuracy and quality of instruction tuning pairs by improving keyframe extraction, leveraging SoTA… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI/video_annotation_pipeline.Video-Bench_human_annotation
Video Generation Model Evaluation Dataset
This dataset contains human annotations for videos generated by different video generation models. The annotations evaluate the quality of generated videos across multiple dimensions.
Dataset Structure
Each JSON file represents one evaluation dimension and follows this structure:
Key Components
prompt_en: The English text prompt used to generate the videos
videos: Paths to video files generated by different models… See the full description on the dataset page: https://huggingface.co/datasets/Video-Bench/Video-Bench_human_annotation.