Kavli Affiliate: Wei Gao
| First 5 Authors: Zhiyi Pan, Nan Zhang, Wei Gao, Shan Liu, Ge Li
| Summary:
Utilizing uniformly distributed sparse annotations, weakly supervised
learning alleviates the heavy reliance on fine-grained annotations in point
cloud semantic segmentation tasks. However, few works discuss the inhomogeneity
of sparse annotations, albeit it is common in real-world scenarios. Therefore,
this work introduces the probability density function into the gradient
sampling approximation method to qualitatively analyze the impact of annotation
sparsity and inhomogeneity under weakly supervised learning. Based on our
analysis, we propose an Adaptive Annotation Distribution Network (AADNet)
capable of robust learning on arbitrarily distributed sparse annotations.
Specifically, we propose a label-aware point cloud downsampling strategy to
increase the proportion of annotations involved in the training stage.
Furthermore, we design the multiplicative dynamic entropy as the gradient
calibration function to mitigate the gradient bias caused by non-uniformly
distributed sparse annotations and explicitly reduce the epistemic uncertainty.
Without any prior restrictions and additional information, our proposed method
achieves comprehensive performance improvements at multiple label rates and
different annotation distributions.
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