  This paper reports a new solution of leveraging temporal classification to
support weakly supervised object detection (WSOD). Specifically, we introduce
raster scan-order techniques to serialize 2D images into 1D sequence data, and
then leverage a combined LSTM (Long, Short-Term Memory) and CTC (Connectionist
Temporal Classification) network to achieve object localization based on a
total count (of interested objects). We term our proposed network LSTM-CCTC
(Count-based CTC). This "learning from counting" strategy differs from existing
WSOD methods in that our approach automatically identifies critical points on
or near a target object. This strategy significantly reduces the need of
generating a large number of candidate proposals for object localization.
Experiments show that our method yields state-of-the-art performance based on
an evaluation on PASCAL VOC datasets.
