We conduct detailed analysis of the main components that lead to high transfer performance.
SOTA for Image Classification on ObjectNet (Bounding Box) (using extra training data)
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
While image classification models have recently continued to advance, most downstream applications such as object detection and semantic segmentation still employ ResNet variants as the backbone network due to their simple and modular structure.
SOTA for Semantic Segmentation on ADE20K
To bridge the domain gap to real imagery with no labeling, we train another matting network guided by the first network and by a discriminator that judges the quality of composites.
SOTA for Image Matting on Adobe Matting