  Weakly supervised object detection is a challenging task when provided with
image category supervision but required to learn, at the same time, object
locations and object detectors. The inconsistency between the weak supervision
and learning objectives introduces significant randomness to object locations
and ambiguity to detectors. In this paper, a min-entropy latent model (MELM) is
proposed for weakly supervised object detection. Min-entropy serves as a model
to learn object locations and a metric to measure the randomness of object
localization during learning. It aims to principally reduce the variance of
learned instances and alleviate the ambiguity of detectors. MELM is decomposed
into three components including proposal clique partition, object clique
discovery, and object localization. MELM is optimized with a recurrent learning
algorithm, which leverages continuation optimization to solve the challenging
non-convexity problem. Experiments demonstrate that MELM significantly improves
the performance of weakly supervised object detection, weakly supervised object
localization, and image classification, against the state-of-the-art
approaches.
