This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning.
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples.
Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.
We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection.
Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e. g., beaks for bird species).
Ranked #22 on Fine-Grained Image Classification on FGVC Aircraft