Global Sum Pooling: A Generalization Trick for Object Counting with\n Small Datasets of Large Images
Bibliographic record
Abstract
In this paper, we explore the problem of training one-look regression models\nfor counting objects in datasets comprising a small number of high-resolution,\nvariable-shaped images. We illustrate that conventional global average pooling\n(GAP) based models are unreliable due to the patchwise cancellation of true\noverestimates and underestimates for patchwise inference. To overcome this\nlimitation and reduce overfitting caused by the training on full-resolution\nimages, we propose to employ global sum pooling (GSP) instead of GAP or fully\nconnected (FC) layers at the backend of a convolutional network. Although\ncomputationally equivalent to GAP, we show through comprehensive\nexperimentation that GSP allows convolutional networks to learn the counting\ntask as a simple linear mapping problem generalized over the input shape and\nthe number of objects present. This generalization capability allows GSP to\navoid both patchwise cancellation and overfitting by training on small patches\nand inference on full-resolution images as a whole. We evaluate our approach on\nfour different aerial image datasets - two car counting datasets (CARPK and\nCOWC), one crowd counting dataset (ShanghaiTech; parts A and B) and one new\nchallenging dataset for wheat spike counting. Our GSP models improve upon the\nstate-of-the-art approaches on all four datasets with a simple architecture.\nAlso, GSP architectures trained with smaller-sized image patches exhibit better\nlocalization property due to their focus on learning from smaller regions while\ntraining.\n
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".