1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
ViewPCL: a point cloud based active learning method for multi-view segmentation
Christian Hilaire, Sima Didari
We propose a novel active learning framework for multi-view semantic segmentation. This framework relies on a new score that measures the discrepancy between point cloud distributi…
Bayesian Active Learning for Semantic Segmentation
Sima Didari, Wenjun Hu, Jae Oh Woo +3
Fully supervised training of semantic segmentation models is costly and challenging because each pixel within an image needs to be labeled. Therefore, the sparse pixel-level annota…
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation
Ayaan Haque, Hankyu Moon, Heng Hao +3
3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats. Triangular meshes are an efficient representation for irregular, non-uni…
PatchNet: Unsupervised Object Discovery based on Patch Embedding
Hankyu Moon, Heng Hao, Sima Didari +2
We demonstrate that frequently appearing objects can be discovered by training randomly sampled patches from a small number of images (100 to 200) by self-supervision. Key to this…
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification
Heng Hao, Hankyu Moon, Sima Didari +2
We propose a highly data-efficient active learning framework for image classification. Our novel framework combines: (1) unsupervised representation learning of a Convolutional Neu…