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[GH-ISSUE #34] Paper: Surveillance section #32
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Originally created by @nikky4D on GitHub (Feb 3, 2021).
Original GitHub issue: https://github.com/openai/CLIP/issues/34
Thank you for your paper. It is very interesting. I especially like the additional sections on Broader Impact and Limitations as they are very detailed.
I have a question on the surveillance section 7.2: Could you expand on what you did in setting up the coarse and fine grained classification? I don't understand what the "close" text description is.
If I understood correctly, in section 7.2, you collect 515 CCTV images. You get groundtruth captions by hand captioning these images. Then giving the CLIP model a CCTV image, and 6 different input text descriptions, you predict the closest matching text to the input image. In addition, you sometimes include a "close" text description.
So, how is the "close" text description different from the given 6 options and the groundtruth? Does the "close" text contain an element of the groundtruth hence why the model keeps choosing the "close" text?
@nikky4D commented on GitHub (Feb 10, 2021):
FIgured it out from colab. Thanks