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wzuidema
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141b0e4
Update app.py
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app.py
CHANGED
@@ -60,9 +60,8 @@ inputs = [input_img, input_txt]
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outputs = [gr.inputs.Image(type='pil', label="Output Image"), "highlight"]
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description = """
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<br> <br>
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This demo shows attributions scores on both the image and the text input when presenting CLIP with a
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<text,image> pair. Attributions are computed as Gradient-weighted Attention Rollout (Chefer et al.,
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2021), and can be thought of as an estimate of the effective attention CLIP pays to its input when
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@@ -155,4 +154,4 @@ with demo_tabs:
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\[2\]: Abnar, S., & Zuidema, W. (2020). Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928. <br>
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\[3\]: [https://samiraabnar.github.io/articles/2020-04/attention_flow](https://samiraabnar.github.io/articles/2020-04/attention_flow) <br>
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""")
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demo_tabs.launch(show_error=True)
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outputs = [gr.inputs.Image(type='pil', label="Output Image"), "highlight"]
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description = """This demo is a copy of the demo CLIPGroundingExlainability built by Paul Hilders, Danilo de Goede and Piyush Bagad, as part of the course Interpretability and Explainability in AI (MSc AI, UvA, June 2022).
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<br> <br>
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This demo shows attributions scores on both the image and the text input when presenting CLIP with a
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<text,image> pair. Attributions are computed as Gradient-weighted Attention Rollout (Chefer et al.,
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2021), and can be thought of as an estimate of the effective attention CLIP pays to its input when
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\[2\]: Abnar, S., & Zuidema, W. (2020). Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928. <br>
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\[3\]: [https://samiraabnar.github.io/articles/2020-04/attention_flow](https://samiraabnar.github.io/articles/2020-04/attention_flow) <br>
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""")
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demo_tabs.launch(show_error=True)
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