base on [ICCV 2023] Tracking Anything with Decoupled Video Segmentation # DEVA: Tracking Anything with Decoupled Video Segmentation  [Ho Kei Cheng](https://hkchengrex.github.io/), [Seoung Wug Oh](https://sites.google.com/view/seoungwugoh/), [Brian Price](https://www.brianpricephd.com/), [Alexander Schwing](https://www.alexander-schwing.de/), [Joon-Young Lee](https://joonyoung-cv.github.io/) University of Illinois Urbana-Champaign and Adobe ICCV 2023 [[arXiV]](https://arxiv.org/abs/2309.03903) [[PDF]](https://arxiv.org/pdf/2309.03903.pdf) [[Project Page]](https://hkchengrex.github.io/Tracking-Anything-with-DEVA/) [](https://colab.research.google.com/drive/1OsyNVoV_7ETD1zIE8UWxL3NXxu12m_YZ?usp=sharing) ## Highlights 1. Provide long-term, open-vocabulary video segmentation with text-prompts out-of-the-box. 2. Fairly easy to **integrate your own image model**! Wouldn't you or your reviewers be interested in seeing examples where your image model also works well on videos :smirk:? No finetuning is needed! ***Note (Mar 6 2024):*** We have fixed a major bug (introduced in the last update) that prevented the deletion of unmatched segments in text/eval_with_detections modes. This should greatly reduce the amount of accumulated noisy detection/false positives, especially for long videos. See [#64](https://github.com/hkchengrex/Tracking-Anything-with-DEVA/issues/64). ***Note (Sep 12 2023):*** We have improved automatic video segmentation by not querying the points in segmented regions. We correspondingly increased the number of query points per side to 64 and deprecated the "engulf" mode. The old code can be found in the "legacy_engulf" branch. The new code should run a lot faster and capture smaller objects. The text-prompted mode is still recommended for better results. ***Note (Sep 11 2023):*** We have removed the "pluralize" option as it works weirdly sometimes with GroundingDINO. If needed, please pluralize the prompt yourself. ## Abstract We develop a decoupled video segmentation approach (**DEVA**), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we propose a (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several tasks, most notably in large-vocabulary video panoptic segmentation and open-world video segmentation. ## Demo Videos ### Demo with Grounded Segment Anything (text prompt: "guinea pigs" and "chicken"): https://github.com/hkchengrex/Tracking-Anything-with-DEVA/assets/7107196/457a9a6a-86c3-4c5a-a3cc-25199427cd11 Source: https://www.youtube.com/watch?v=FM9SemMfknA ### Demo with Grounded Segment Anything (text prompt: "pigs"): https://github.com/hkchengrex/Tracking-Anything-with-DEVA/assets/7107196/9a6dbcd1-2c84-45c8-ac0a-4ad31169881f Source: https://youtu.be/FbK3SL97zf8 ### Demo with Grounded Segment Anything (text prompt: "capybara"): https://github.com/hkchengrex/Tracking-Anything-with-DEVA/assets/7107196/2ac5acc2-d160-49be-a013-68ad1d4074c5 Source: https://youtu.be/couz1CrlTdQ ### Demo with Segment Anything (automatic points-in-grid prompting); original video follows DEVA result overlaying the video: https://github.com/hkchengrex/Tracking-Anything-with-DEVA/assets/7107196/ac6ab425-2f49-4438-bcd4-16e4ccfb0d98 Source: DAVIS 2017 validation set "soapbox" ### Demo with Segment Anything on a out-of-domain example; original video follows DEVA result overlaying the video: https://github.com/hkchengrex/Tracking-Anything-with-DEVA/assets/7107196/48542bcd-113c-4454-b512-030df26def08 Source: https://youtu.be/FQQaSyH9hZI ## Installation Tested on Ubuntu only. For installation on Windows WSL2, refer to https://github.com/hkchengrex/Tracking-Anything-with-DEVA/issues/20 (thanks @21pl). **Prerequisite:** - Python 3.9+ - PyTorch 1.12+ and corresponding torchvision **Clone our repository:** ```bash git clone https://github.com/hkchengrex/Tracking-Anything-with-DEVA.git ``` **Install with pip:** ```bash cd Tracking-Anything-with-DEVA pip install -e . ``` (If you encounter the `File "setup.py" not found` error, upgrade your pip with `pip install --upgrade pip`) **Download the pretrained models:** ```bash bash scripts/download_models.sh ``` **Required for the text-prompted/automatic demo:** Install [our fork of Grounded-Segment-Anything](https://github.com/hkchengrex/Grounded-Segment-Anything). Follow its instructions. Grounding DINO installation might fail silently. Try `python -c "from groundingdino.util.inference import Model as GroundingDINOModel"`. If you get a warning about running on CPU mode only, make sure you have `CUDA_HOME` set during Grounding DINO installation. **(Optional) For fast integer program solving in the semi-online setting:** Get your [gurobi](https://www.gurobi.com/) licence which is free for academic use. If a license is not found, we fall back to using [PuLP](https://github.com/coin-or/pulp) which is slower and is not rigorously tested by us. All experiments are conducted with gurobi. ## Quick Start [DEMO.md](docs/DEMO.md) contains more details on the input arguments and tips on speeding up inference. You can always look at `deva/inference/eval_args.py` and `deva/ext/ext_eval_args.py` for a full list of arguments. **With gradio:** ```bash python demo/demo_gradio.py ``` Then visit the link that popped up on the terminal. If executing on a remote server, try [port forwarding](https://unix.stackexchange.com/questions/115897/whats-ssh-port-forwarding-and-whats-the-difference-between-ssh-local-and-remot). We have prepared an example in `example/vipseg/12_1mWNahzcsAc` (a clip from the VIPSeg dataset). The following two scripts segment the example clip using either Grounded Segment Anything with text prompts or SAM with automatic (points in grid) prompting. **Script (text-prompted):** ```bash python demo/demo_with_text.py --chunk_size 4 \ --img_path ./example/vipseg/images/12_1mWNahzcsAc \ --amp --temporal_setting semionline \ --size 480 \ --output ./example/output --prompt person.hat.horse ``` We support different SAM variants in **text-prompted modes**, by default we use original sam version. For **higher-quality** masks prediction, you specify `--sam_variant sam_hq`. For **running efficient** sam usage, you can specify `--sam_variant sam_hq_light` or `--sam_variant mobile`. **Script (automatic):** ```bash python demo/demo_automatic.py --chunk_size 4 \ --img_path ./example/vipseg/images/12_1mWNahzcsAc \ --amp --temporal_setting semionline \ --size 480 \ --output ./example/output ``` ## Training and Evaluation 1. [Running DEVA with your own detection model.](docs/CUSTOM.md) 2. [Running DEVA with detections to reproduce the benchmark results.](docs/EVALUATION.md) 3. [Training the DEVA model.](docs/TRAINING.md) ## Limitations - On closed-set data, DEVA most likely does not work as well as end-to-end approaches. Joint training is (for now) still a better idea when you have enough target data. - Positive detections are amplified temporally due to propagation. Having a detector with a lower false positive rate (i.e., a higher threshold) helps. - If new objects are coming in and out all the time (e.g., in driving scenes), we will keep a lot of objects in the memory bank which unfortunately increases the false positive rate. Decreasing `max_missed_detection_count` might help since we delete objects from memory more eagerly. <picture> <source media="(prefers-color-scheme: dark)" srcset="https://imgur.com/aouI1WU.png"> <source media="(prefers-color-scheme: light)" srcset="https://imgur.com/aCbrA9S.png"> <img alt="separator" src="https://imgur.com/aCbrA9S.png"> </picture> ## Citation ```bibtex @inproceedings{cheng2023tracking, title={Tracking Anything with Decoupled Video Segmentation}, author={Cheng, Ho Kei and Oh, Seoung Wug and Price, Brian and Schwing, Alexander and Lee, Joon-Young}, booktitle={ICCV}, year={2023} } ``` ## References The demo would not be possible without :heart: from the community: Grounded Segment Anything: https://github.com/IDEA-Research/Grounded-Segment-Anything Segment Anything: https://github.com/facebookresearch/segment-anything XMem: https://github.com/hkchengrex/XMem Title card generated with OpenPano: https://github.com/ppwwyyxx/OpenPano ", Assign "at most 3 tags" to the expected json: {"id":"679","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"