Sparse Shape Encoding for Topologically Improved Instance Segmentation
Bibliographic record
Abstract
Neurophysiological studies suggest that neurons in the intermediate visual area V4 of the primate cortex encode a sparse representation of object shape. While there are metabolic arguments for such sparse representations, there are also potential advantages for inference. Here we explore whether sparse shape encoding can yield benefits for object instance segmentation. Specifically, we encode 2D object shape using a Distance Transform Map (DTM) and learn a sparse basis for this representation. To make use of this encoding, we design and train an instance segmentation head to estimate the sparse coefficients representing the shape of each object, and then recover the estimated shape from the zero-crossing level set of the corresponding DTM. Our novel SparseShape encoding approach produces fewer topological errors than the state of the art, yields competitive mask AP on the COCO benchmark and exhibits superior generalization performance on the Cityscapes instance segmentation task. These results suggest that the sparse shape encoding observed in primate cortex has computational advantages that can benefit computer vision instance segmentation systems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".