SDSimPoint: Shallow-Deep Similarity Learning for Few-Shot Point Cloud Semantic Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Three-dimensional point cloud semantic segmentation is a fundamental task in computer vision. As the fully supervised approaches suffer from the generalization issue with limited data, few-shot point cloud segmentation models have been proposed to address the flexible adaptation. Nevertheless, due to the class-agnostic nature of the few-shot pretraining, its pretrained feature extractor is hard to capture the class-related intrinsic and abstract information. Therefore, we introduce the new concept of shallow and deep similarities and propose a shallow-deep similarity learning network (SDSimPoint) that aims to learn both shallow (superficial geometry, color, etc.) and deep similarities (intrinsic context and semantics, etc.) between the support and query samples, thereby boosting the performance. Moreover, we design a beyond-episode attention module (BEAM) to enlarge the region of the attention mechanism from a single episode to the entire dataset by utilizing the memory units, which enhances the extraction ability to better capture the shallow and deep similarities. Furthermore, our distance metric function is learnable in the proposed framework, which can better adapt to complex data distributions. Our proposed SDSimPoint consistently demonstrates substantial improvements compared to baseline approaches across various datasets in diverse few-shot point cloud semantic segmentation settings.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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