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Update app.py
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app.py
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@@ -157,6 +157,19 @@ description = "This notebook provides a (limited) hands-on demonstration of MTTR
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article = "**Disclaimer:** <br> This is a **limited** demonstration of MTTR's performance. The model used here was trained **exclusively** on Refer-YouTube-VOS with window size `w=12` (as described in our paper). No additional training data was used whatsoever. Hence, the model's performance may be limited, especially on instances from unseen categories. <br> Additionally, slow processing times may be encountered, depending on the input clip length and/or resolution, and due to HuggingFace's limited computational resources (no GPU acceleration unfortunately). <br> Finally, we emphasize that this demonstration is intended to be used for academic purposes only. We do not take any responsibility for how the created content is used or distributed. <br> <p style='text-align: center'><a href='https://github.com/mttr2021/MTTR'>Github Repo</a></p>"
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# article = "<p style='text-align: center'><a href='https://github.com/mttr2021/MTTR'>Github Repo</a></p>"
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# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.14821'>End-to-End Referring Video Object Segmentation with Multimodal Transformers</a> | <a href='https://github.com/mttr2021/MTTR'>Github Repo</a></p>"
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@@ -166,7 +179,7 @@ iface = gr.Interface(fn=process,
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title=title,
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description=description,
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enable_queue=True,
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examples=
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article=article)
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iface.launch(debug=True)
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article = "**Disclaimer:** <br> This is a **limited** demonstration of MTTR's performance. The model used here was trained **exclusively** on Refer-YouTube-VOS with window size `w=12` (as described in our paper). No additional training data was used whatsoever. Hence, the model's performance may be limited, especially on instances from unseen categories. <br> Additionally, slow processing times may be encountered, depending on the input clip length and/or resolution, and due to HuggingFace's limited computational resources (no GPU acceleration unfortunately). <br> Finally, we emphasize that this demonstration is intended to be used for academic purposes only. We do not take any responsibility for how the created content is used or distributed. <br> <p style='text-align: center'><a href='https://github.com/mttr2021/MTTR'>Github Repo</a></p>"
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examples = [['guy in white shirt performing tricks on a bike', 'bike_tricks_2.mp4'],
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['a man riding a surfboard', 'surfing.mp4'],
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['a guy performing tricks on a skateboard', 'skateboarding.mp4'],
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['man in red shirt playing tennis', 'tennis.mp4'],
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['brown and black dog playing', 'dogs_playing_1.mp4'],
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['a dog to the left playing with a toy', 'dogs_playing_2.mp4'],
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['person in blue riding a bike', 'blue_biker_riding.mp4'],
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['a dog to the right', 'dog_and_cat.mp4'],
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['a person hugging a dog', 'girl_hugging_dog.mp4'],
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['a black bike used to perform tricks', 'bike_tricks_1.mp4'],
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['a black horse playing with a person', 'horse_plays_ball.mp4'] ]
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# article = "<p style='text-align: center'><a href='https://github.com/mttr2021/MTTR'>Github Repo</a></p>"
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# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.14821'>End-to-End Referring Video Object Segmentation with Multimodal Transformers</a> | <a href='https://github.com/mttr2021/MTTR'>Github Repo</a></p>"
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title=title,
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description=description,
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enable_queue=True,
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examples=examples,
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article=article)
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iface.launch(debug=True)
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