Domain Adaptation in Crowd Counting
Bibliographic record
Abstract
We consider the problem of domain adaptation in crowd counting. Given an input image of a crowd scene, our goal is to estimate the count of people in the image. Previous work in crowd counting usually assumes that training and test images are captured by the same camera. We argue that this is not realistic in real-world applications of crowd counting. In this paper, we consider a domain adaptation setting in crowd counting where we have a source domain and a target domain. For example, these two domains might correspond to cameras at two different locations (i.e., with differing viewpoints, illumination conditions, environment objects, crowd densities, etc.). We have enough labeled training data from the source domain, but we only have either unlabeled data or a small number of labeled data in the target domain. Our goal is to train a crowd counting system that performs well in the target domain. We believe this setting is closer to real-world deployment of crowd counting systems. Due to the domain shift, a model trained from the source domain is unlikely to perform well in the target domain. In this paper, we propose several domain adaptation techniques for this problem. Our experimental results demonstrate the superior performance of our proposed approach on several benchmark datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".