Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks
Bibliographic record
Abstract
There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection. This task has received significantly less attention in the literature than its supervised counterpart, especially in the case of large images containing many objects. In the current work, we develop a neural network architecture that effectively addresses this large-image, many-object setting. In particular, we combine ideas from Attend, Infer, Repeat (AIR), which performs unsupervised object detection but does not scale well, with recent developments in supervised object detection. We replace AIR’s core recurrent network with a convolutional (and thus spatially invariant) network, and make use of an object-specification scheme that describes the location of objects with respect to local grid cells rather than the image as a whole. Through a series of experiments, we demonstrate a number of features of our architecture: that, unlike AIR, it is able to discover and detect objects in large, many-object scenes; that it has a significant ability to generalize to images that are larger and contain more objects than images encountered during training; and that it is able to discover and detect objects with enough accuracy to facilitate non-trivial downstream processing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".