Refining Naive Annotations with Limited Expert Guidance for Semantic Segmentation: A Case Study on Underwater Echograms
Bibliographic record
Abstract
Training models for supervised semantic segmentation typically requires large quantities of pixel-level annotations that are difficult to assemble in many application domains, in particular when metadata is scarce. In the context of underwater echogam analysis for environmental monitoring, metadata scarcity is manifested by a lack of ground truth and limited domain expert resources, hindering standard data annotation processes. We propose an iterative, non-interactive annotation approach that allows us to obtain large quantities of echogram annotations using minimal expert guidance. In a two-stage process, a segmentation neural network is first purposely overfitted to a very small expertly annotated set, and is then used to iteratively refine a larger set of rough, naive annotations. Experiments on the Cape Bathurst Arctic Sea Surface Acoustics (CBASSA) dataset showcase our method's capability to generate annotations for the sea surface and subsurface entrained air bubbles that approach expert quality level (within 5.5 p.p. for the intersection over union and 3 p.p. for the F1-score), starting from simple non-expert lines obtained at a fraction of the time required by experts. They also show that our method is compatible with both convolutional- and transformer-based neural networks, and pave the way for annotating large datasets resulting from long/continuous deployments for underwater environmental monitoring, at minimal cost.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".