Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world.
Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance.
To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors.
#9 best model for Instance Segmentation on COCO test-dev
We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal.
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization.
SOTA for Object Detection on COCO minival
We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.