Browse State-of-the-Art › Zero-Guidance Segmentation
Zero-Guidance Segmentation
1 paper with code · 0 benchmarks · 0 datasets archive 2025-07-28
The task aiming to discover semantic segments without any user guidance in the form of text queries or predefined classes, and label them using natural language automatically
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
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Subtasks archive 2025-07-28
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Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
1 shown of 1 paper with code (1 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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23 Mar 2023 1 repository listedCLIP has enabled new and exciting joint vision-language applications, one of which is open-vocabulary segmentation, which can locate any segment given an arbitrary text query.
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