Abstract
Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-scale dense prediction tasks with long-tailed category distributions, where pixel- or voxel-level annotation is prohibitively expensive. We propose SASS (Stage-Adaptive Sample Selection), a stage-adaptive data-selection framework for pool-based active learning in long-tailed dense prediction. SASS combines three components: label-free self-supervised gradient scoring, prior-guided category rebalancing with validation-driven feedback, and stage-adaptive acquisition aligned with model training dynamics. This design avoids candidate ground-truth masks during gradient scoring while making acquisition responsive to long-tail imbalance and evolving representations. We evaluate SASS on a multimodal 3D medical segmentation testbed comprising over 100,000 samples spanning 108 anatomical structures. SASS recovers 98.3% of full-dataset performance with a 40% training-pool annotation budget, outperforming BADGE by 5.1 percentage points. Moreover, SASS exhibits a statistically supported less-is-more pattern, surpassing full-dataset training at the Hard-group level and, at the structure level, for the pancreas and gallbladder. More broadly, SASS shows that annotation-efficient learning depends not only on which samples are selected, but also on how the annotation budget is distributed across categories and when model-derived scores begin to guide selection.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Du, X., Zhang, L., Yan, S., Wang, M., & Song, Z. (2026). Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction. https://omanscience.com/en/articles/less-is-more-in-the-long-tail-stage-adaptive-sample-selection-for-annotation-efficient-dense-prediction
MLA 9
Du, Xiaofei, et al. "Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction." https://omanscience.com/en/articles/less-is-more-in-the-long-tail-stage-adaptive-sample-selection-for-annotation-efficient-dense-prediction.
Chicago (author–date)
Du, Xiaofei, Lei Zhang, Shuyu Yan, Manning Wang, and Zhijian Song. 2026. "Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction." https://omanscience.com/en/articles/less-is-more-in-the-long-tail-stage-adaptive-sample-selection-for-annotation-efficient-dense-prediction.
Harvard
Du, X., Zhang, L., Yan, S., Wang, M. and Song, Z. (2026) 'Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction', Available at: https://omanscience.com/en/articles/less-is-more-in-the-long-tail-stage-adaptive-sample-selection-for-annotation-efficient-dense-prediction.
Vancouver
Du X, Zhang L, Yan S, Wang M, Song Z. Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction. https://omanscience.com/en/articles/less-is-more-in-the-long-tail-stage-adaptive-sample-selection-for-annotation-efficient-dense-prediction
IEEE
X. Du, L. Zhang, S. Yan, M. Wang, and Z. Song, "Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction," https://omanscience.com/en/articles/less-is-more-in-the-long-tail-stage-adaptive-sample-selection-for-annotation-efficient-dense-prediction.