Tuner: A New Approach For 3D Semantic Segmentation Using Federated Architecture
Bibliographic record
Abstract
3D computer vision (CV) applications have been gaining great popularity in various real-world applications, such as autonomous driving, environmental surveillance, and medical diagnostics. A significant obstacle in advancing 3D CV applications is the scarcity of large-scale, high-quality training datasets. Gathered 3D datasets have the issue of data heterogeneity since comprehensive capturing of 3D representations requires specialized tools and systematic approaches like scanning the object or scene from multiple viewpoints. This frequently results in datasets that are not only limited in size but also exhibit significant variation in attributes like resolutions, lighting conditions, and the distribution of labels. To address these challenges, this paper introduces Tuner, a novel strategy for training 3D CV models using datasets from various sources. Tuner leverages a federated learning (FL) framework, allowing the incorporation of extensive data samples. It achieves this through a unique and streamlined model structure linked to each participant’s model, designed to effectively address the issue of data heterogeneity. Our experiments show that Tuner can outperform other FL algorithms on various 3D segmentation tasks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".