Learning universal semantic correspondences with no supervision and automatic data curation
We study the problem of learning semantic image correspondences without manual supervision. Previous works that tackled this problem rely on manually curated image pairs and learn benchmark-specific correspondences. Instead, we present a new method that learns universal correspondences once, from a...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2023
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