Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networks
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Bibliographic record
Abstract
Despite the recent successes in computer vision, there remain new avenues to explore. In this work, we propose a new dataset to investigate the effect of self-occlusion on deep neural networks. With TEOS (The Effect of Self-Occlusion), we propose a 3D blocks world dataset that focuses on the geometric shape of 3D objects and their omnipresent self-occlusion. We designed TEOS to investigate the role of self-occlusion in the context of object classification. In the real-world, self-occlusion of 3D objects still presents significant challenges for deep learning approaches. However, humans deal with this by deploying complex strategies, for instance, by changing the viewpoint or manipulating the scene to gather necessary information. With TEOS, we present a dataset with two subsets (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ), containing 36 and 12 objects, respectively. We provide 768 uniformly sampled views of each object, their mask, object and camera position, orientation, amount of self-occlusion, as well as the CAD model of each object. We present baseline evaluations with five well-known classification deep neural networks and show that TEOS poses a significant challenge for all of them. The dataset, as well as the pre-trained models, are made publicly available for the scientific community under https: //data.nvision.eecs.yorku.ca/TEOS.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it