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UIE

39 papers with code · 28 benchmarks · 13 datasets archive 2025-07-28

Computer Vision

Underwater image enhancement is a technique used to improve the quality of underwater images. Due to the unique properties of the underwater environment, images captured underwater often suffer from degradation caused by absorption and scattering of light. These effects can result in low contrast, blurred images with a dominant blue or green color cast. Enhancement techniques aim to correct these issues and improve the visibility within the image. These methods can include color correction to remove the color cast, contrast enhancement to improve the visibility of underwater objects, and dehazing techniques to reduce the scattering effect. These enhancements are crucial in various applications, including underwater exploration, marine biology research, underwater archaeology, and in improving the performance of underwater vision systems used in autonomous underwater vehicles.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

28 leaderboard tables shown for this task, 28 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 28 until expanded.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
ACE 2004 (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
ACE 2005-EAE (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
ACE 2005-ED (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
ACE 2005-NER (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
ACE 2005-RE (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
ADE Corpus (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
AnatEM (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
BC2GM (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
BC5CDR (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
Broad Twitter (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
CoNLL 2003 (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
CoNLL 2004 (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
DIANN (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
FabNER (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
FindVehicle (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
GENIA (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
GIDS (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
kbp37 (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
MIT Movie (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
MIT Restaurant (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
MultiNERD (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
ncbi_disease (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
NYT (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
OntoNotes 5.0 (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
SciERC (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
semeval RE (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
WikiANN (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — Compare
WNUT 2017 (1 row) KnowCoder-7b-IE KnowCoder: Coding Structured Knowledge into LLMs for Universal... code — 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

13 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

30 shown of 39 papers with code (56 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 6 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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