9 citations · 28 across the 15 of their papers we have counts for
4 papers · 1 filter
A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation
Yixiao Zhang, Adam Kortylewski, Qing Liu +9
The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated…
Visual analogy: Deep learning versus compositional models
Nicholas Ichien, Qing Liu, Shuhao Fu +3
Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by comp…
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
Qing Liu, Vignesh Ramanathan, Dhruv Mahajan +2
Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predomina…
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
Qing Liu, Adam Kortylewski, Zhishuai Zhang +7
Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply s…