Simultaneous Object Detection and Ranking with Weak Supervision
Matthew Blaschko, Andrea Vedaldi and Andrew Zisserman
In: NIPS 2010, 6 Dec - 9 Dec 2010, Vancouver.
A standard approach to learning object category detectors is to provide strong supervision in the form of a region of interest (ROI) specifying each instance of the object in the training images . In this work are goal is to learn from heterogeneous labels, in which some images are only weakly supervised, specifying only the presence or absence of the object or a weak indication of object location, whilst others are fully annotated.
To this end we develop a discriminative learning approach and make two contributions: (i) we propose a structured output formulation for weakly annotated images where full annotations are treated as latent variables; and (ii) we propose to optimize
a ranking objective function, allowing our method to more effectively use negatively labeled images to improve detection average precision performance.
The method is demonstrated on the benchmark INRIA pedestrian detection dataset
of Dalal and Triggs  and the PASCAL VOC dataset , and it is shown that
for a significant proportion of weakly supervised images the performance achieved
is very similar to the fully supervised (state of the art) results.