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Open Vocabulary Semantic Segmentation

71 papers with code · 14 benchmarks · 6 datasets archive 2025-07-28

Computer Vision

Open-vocabulary semantic segmentation models aim to accurately assign a semantic label to each pixel in an image from a set of arbitrary open-vocabulary texts.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

15 leaderboard tables shown for this task, 14 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. 10 shown of 15 until expanded.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
PASCAL Context-59 (24 rows) HyperSeg HyperSeg: Towards Universal Visual Segmentation with Large Language Model code Syntology ran 7 of 17 samples · 10 unverified Compare
ADE20K-150 (23 rows) Mask-Adapter Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation code — Compare
PascalVOC-20 (20 rows) UMG-CLIP-L/14 UMG-CLIP: A Unified Multi-Granularity Vision Generalist for... code — Compare
ADE20K-847 (19 rows) UMG-CLIP-E/14 UMG-CLIP: A Unified Multi-Granularity Vision Generalist for... code — Compare
PASCAL Context-459 (15 rows) SILC SILC: Improving Vision Language Pretraining with Self-Distillation — — Compare
COCO-Stuff-171 (7 rows) POMP Prompt Pre-Training with Twenty-Thousand Classes for... code Syntology ran 5 of 7 samples · 2 unverified Compare
Cityscapes (5 rows) FC-CLIP Convolutions Die Hard: Open-Vocabulary Segmentation with Single... code Syntology ran 1 of 2 samples · 1 unverified Compare
PascalVOC-20b (4 rows) UMG-CLIP-E/14 UMG-CLIP: A Unified Multi-Granularity Vision Generalist for... code — Compare
iSAID (2 rows) SkySense-O SkySense: A Multi-Modal Remote Sensing Foundation Model Towards... code — Compare
ISPRS Potsdam (1 row) SkySense-O SkySense: A Multi-Modal Remote Sensing Foundation Model Towards... code — Compare
Cityscape-171 (1 row) PACL Open Vocabulary Semantic Segmentation with Patch Aligned... code — Compare
FAST (1 row) SkySense-O SkySense: A Multi-Modal Remote Sensing Foundation Model Towards... code — Compare
SIOR (1 row) SkySense-O SkySense: A Multi-Modal Remote Sensing Foundation Model Towards... code — Compare
SOTA (1 row) SkySense-O SkySense: A Multi-Modal Remote Sensing Foundation Model Towards... code — Compare
ADE20K-150 (0 rows) no rows in the archive — —

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

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 71 papers with code (113 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.

Syntology lines on 11 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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