Practical Solutions for Label-Efficient Segmentation
Addressing Challenges in 2D and 3D Data Modalities
Label-efficient segmentation is a critical research area in AI, especially for point cloud semantic segmentation. Deep learning techniques have advanced this field, but the reliance on large-scale datasets with point-wise annotations remains a challenge.
Recent methods have explored weak supervision, human annotations, and techniques like perturbed self-distillation and consistency regularization to tackle this issue. Pseudo-labeling has also gained prominence as an effective strategy for utilizing unlabeled data.
However, existing methods often involve complex training processes and focus primarily on 2D image segmentation, neglecting the challenges of 3D point clouds with sparse labels.
One novel approach, ERDA, incorporates entropy regularization and distribution alignment to optimize pseudo-label generation and segmentation model training simultaneously. This method demonstrates superior performance across various label-efficient settings, outperforming fully supervised baselines with minimal true annotations.
ERDA’s flexibility allows application to various label-efficient segmentation tasks, including semi-supervised, sparse labels, and unsupervised settings. Its straightforward implementation reduces to a cross-entropy-based loss for simplified training.
Experimental results show that ERDA significantly improves performance in unsupervised 2D segmentation settings and achieves notable improvements in 3D tasks, outperforming many fully supervised methods with only 1% of labels.
In conclusion, ERDA is a promising approach for modality-agnostic label-efficient segmentation, showing superior performance across various datasets and modalities. It addresses challenges of insufficient supervision and varying data processing techniques across 2D and 3D modalities, with potential for future research combining label-efficient methods with large foundation models.
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