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- W4386065720 abstract "A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state of the art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which only the image is input, without any text. Our code is available at https://github.com/talshaharabany/Similarity-Maps-for-Self-Training-Weakly-Supervised-Phrase-Grounding" @default.
- W4386065720 created "2023-08-23" @default.
- W4386065720 creator A5021074158 @default.
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- W4386065720 date "2023-06-01" @default.
- W4386065720 modified "2023-09-27" @default.
- W4386065720 title "Similarity Maps for Self-Training Weakly-Supervised Phrase Grounding" @default.
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- W4386065720 doi "https://doi.org/10.1109/cvpr52729.2023.00669" @default.
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