| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - video-object-segmentation |
| - video-segmentation |
| - evaluation |
| - croissant |
| - neurips-2026 |
| - video |
| pretty_name: FaVOS |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # FaVOS |
|
|
| [Project page](https://aidaslab.github.io/FaVOS) | [Paper](https://arxiv.org/abs/2610.02946) |
|
|
| FaVOS is a video object segmentation benchmark with fractional temporal visibility. It contains 200 videos and 279 annotated objects that may disappear and reappear. |
|
|
| | Subset | Videos | Objects | Mean τ | |
| |---|---:|---:|---:| |
| | FaVOS-20 | 100 | 129 | 0.2051 | |
| | FaVOS-40 | 100 | 150 | 0.4048 | |
| | Full | 200 | 279 | 0.3124 | |
|
|
| FaVOS evaluation uses the first annotated frame as the prompt frame. Metrics are |
| computed on the remaining frames only. |
|
|
| Download the dataset: |
|
|
| ```bash |
| huggingface-cli download snu-aidas/FaVOS --repo-type dataset --local-dir data |
| unzip data/JPEGImages.zip -d data |
| unzip data/Annotations.zip -d data |
| ``` |
|
|
| Evaluate predictions against: |
|
|
| ```text |
| data/Annotations/<video_id>/<frame>.png |
| ``` |
|
|
| Use `favos-20.txt` and `favos-40.txt` as the evaluation splits. To compute Volumetric J&F with [eval_jnf_v.py](eval_jnf_v.py): |
|
|
| ```bash |
| python data/eval_jnf_v.py --pred-root predictions --output-dir results/jf_v |
| ``` |
|
|
| ## License |
|
|
| The annotations and the 67 self-collected videos are released under CC BY-NC 4.0. Frames from the 133 YFCC100M videos keep their original licenses. See [LICENSE.md](LICENSE.md). |
|
|
| Contact: csjihwanh@snu.ac.kr |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{hong2026favos, |
| title = {When Predicting Nothing Beats SAM 3: Revisiting Evaluation in Video Object Segmentation}, |
| author = {Hong, Jihwan and Park, Woohyeon and Kim, Jaeik and Do, Jaeyoung}, |
| booktitle = {Advances in Neural Information Processing Systems}, |
| year = {2026}, |
| note = {Evaluations \& Datasets Track} |
| } |
| ``` |
|
|
