  Deep convolution neural networks (CNN) have demonstrated advanced performance
on single-label image classification, and various progress also have been made
to apply CNN methods on multi-label image classification, which requires to
annotate objects, attributes, scene categories etc. in a single shot. Recent
state-of-the-art approaches to multi-label image classification exploit the
label dependencies in an image, at global level, largely improving the labeling
capacity. However, predicting small objects and visual concepts is still
challenging due to the limited discrimination of the global visual features. In
this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to
address this problem. The utilized model includes a fully convolutional
localization architecture to localize the regions that may contain multiple
highly-dependent labels. The localized regions are further sent to the
recurrent neural networks (RNN) to characterize the latent semantic
dependencies at the regional level. Experimental results on several benchmark
datasets show that our proposed model achieves the best performance compared to
the state-of-the-art models, especially for predicting small objects occurred
in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using
bounding box coordinates during training, the experimental results also show
that our RLSD can approach the upper bound without using the bounding-box
annotations, which is more realistic in the real world.
