  Recognizing multiple labels of images is a fundamental but challenging task
in computer vision, and remarkable progress has been attained by localizing
semantic-aware image regions and predicting their labels with deep
convolutional neural networks. The step of hypothesis regions (region
proposals) localization in these existing multi-label image recognition
pipelines, however, usually takes redundant computation cost, e.g., generating
hundreds of meaningless proposals with non-discriminative information and
extracting their features, and the spatial contextual dependency modeling among
the localized regions are often ignored or over-simplified. To resolve these
issues, this paper proposes a recurrent attention reinforcement learning
framework to iteratively discover a sequence of attentional and informative
regions that are related to different semantic objects and further predict
label scores conditioned on these regions. Besides, our method explicitly
models long-term dependencies among these attentional regions that help to
capture semantic label co-occurrence and thus facilitate multi-label
recognition. Extensive experiments and comparisons on two large-scale
benchmarks (i.e., PASCAL VOC and MS-COCO) show that our model achieves superior
performance over existing state-of-the-art methods in both performance and
efficiency as well as explicitly identifying image-level semantic labels to
specific object regions.
