Detector discovery in the wild: Joint multiple instance and representation learning
Detector discovery in the wild: Joint multiple instance and representation learning
We develop methods for detector learning which exploit joint training over both weak (image-level) and strong (bounding box) labels and which transfer learned perceptual representations from strongly-labeled auxiliary tasks. Previous methods for weak-label learning often learn detector models independently using latent variable optimization, but fail to share deep representation knowledge …