Browse State-of-the-Art › Camouflaged Object Segmentation with a Single Task-generic Prompt
Camouflaged Object Segmentation with a Single Task-generic Prompt
2 papers with code · 3 benchmarks · 2 datasets archive 2025-07-28
The previous fully-supervised and weakly-supervised camouflaged object segmentation tasks required a significant amount of annotated data for supervised training to enable models to segment camouflaged objects effectively. However, models like the Segment Anything Model (SAM), which falls under the category of Promptable Segmentation models, can achieve excellent segmentation performance on unseen images with just an instance-specific visual prompt. Nevertheless, for complex scenarios like camouflaged objects, SAM may not perform well even with an instance-specific prompt. Furthermore, the question arises: Is an instance-specific prompt necessary? In more realistic scenarios, where only a task-generic task description is provided as a universally applicable text prompt, how can we improve segmentation across various datasets under the camouflaged object segmentation task?
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| CAMO (3 rows) | RDVP-MSD | — | — | — | Compare |
| COD10K (3 rows) | RDVP-MSD | — | — | — | Compare |
| Chameleon (2 rows) | ProMaC | Leveraging Hallucinations to Reduce Manual Prompt Dependency in... | code | Syntology ran 2 of 9 samples · 7 unverified | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
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
2 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
2 shown of 2 papers with code (2 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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27 Aug 2024 1 repository listed Syntology ran 2 of 9 samples · 7 unverifiedIn this paper, we utilize hallucinations to mine task-related information from images and verify its accuracy for enhancing precision of the generated prompts.
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12 Dec 2023 1 repository listed Syntology ran 8 of 13 samples · 5 unverifiedIn this work, we aim to eliminate the need for manual prompt.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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