TITLE: Pixel Objectness
AUTHOR: Suyog Dutt Jain, Bo Xiong, Kristen Grauman
ASSOCIATION: The University of Texas at Austin
FROM: arXiv:1701.05349
CONTRIBUTIONS
An end-to-end learning framework for foreground object segmentation is proposed. Given a single novel image, a pixel-level mask is produced for all “object-like” regions even for object categories never seen during training.
METHOD
Problem Formulation
Given an RGB image of size $m \times n \times c$ as input, the problem is formulated as densely labeling each pixel in the images as eigher “object” or “background”. The output is a binary map of size $m \times n$.
Dataset
Two different datasets are used including 1) one dataset with explicit boundary-level annotations and 2) one dataset with implicit imagelevel object category annotations.
Training
The network is first trained on a large scale object classification task, such as ImageNet 1000-category classification. This stage can be regarded as training on an implicit labeled dataset. Its image representation has a strong notion of objectness built inside it, even though it never observes any segmentation annotations.
Then the network is trained on PASCAL 2012 segmentation dataset, which is an explicit labeled dataset. The 20 object labels are discarded, and mapped instead to the single generic “object-like” (foreground) label for training.