622 citations · 948 across the 19 of their papers we have counts for
6 papers · 1 filter
Self-Supervised Visual Representation Learning from Hierarchical Grouping
Xiao Zhang, Michael Maire
We create a framework for bootstrapping visual representation learning from a primitive visual grouping capability. We operationalize grouping via a contour detector that partition…
Information-Theoretic Segmentation by Inpainting Error Maximization
Pedro Savarese, Sunnie S. Y. Kim, Michael Maire +2
We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maxi…
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation
Tri Huynh, Simon Kornblith, Matthew R. Walter +2
Self-supervised representation learning has made significant leaps fueled by progress in contrastive learning, which seeks to learn transformations that embed positive input pairs…
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
Chengcheng Wan, Henry Hoffmann, Shan Lu +1
We propose a novel variant of SGD customized for training network architectures that support anytime behavior: such networks produce a series of increasingly accurate outputs over…
Growing Efficient Deep Networks by Structured Continuous Sparsification
Xin Yuan, Pedro Savarese, Michael Maire
We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity objectives. Unlike existing pr…
Pixel Consensus Voting for Panoptic Segmentation
Haochen Wang, Ruotian Luo, Michael Maire +1
The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the Generalized Hough transform. Pixels cast discretized, probabilistic votes fo…