MCTNet: Multiscale Cross-Attention-Based Transformer Network for Semantic Segmentation of Large-Scale Point Cloud
Bibliographic record
Abstract
In this work, we implement a hybrid method to utilize sufficient information by aggregating both fine-grained and globally contextual features for point cloud semantic segmentation with a hierarchical network. By surpassing the defects of convolution operation mainly for extracting low-level features, we combine higher-level cross-attention based Transformer to investigate the importance of long-range relations together with position embedding for multiscale feature representation. Specifically, adding a learnable token to the feature sequence of a layer, a Transformer encoder is first implemented with limited scope to embed these features. Furthermore, instead of performing all-to-all attention, we merely fuse tokens spanning various scales. To improve efficiency, we propose a simple yet efficient token-fusing architecture based on cross-attention, in which the computation of attention maps can be restricted within linear time by only using a token to calculate the query. The cross-attention module can be efficiently aggregated in a multiscale network to further enlarge the scope of the receptive field for attention. Experiments show that our MCTNet achieves promising results on three largest point cloud datasets, DALES, DublinCity and S3DIS datasets. For the DALES benchmark dataset, MCTNet improves the mean intersection-over-union (mIoU) to 83.3% and the overall accuracy (OA) to 98.3%, which outperforms other existing baselines. We also perform abundant ablation studies on various attention and normalization modules and discuss the effect of parameters to validate the descriptive power of cross-attention module and provide an understanding of how long-range dependency can be used to learn fair and unbiased features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".