Multi-label image classification, which can be categorized into
label-dependency and region-based methods, is a challenging problem due to the
complex underlying object layouts. Although region-based methods are less
likely to encounter issues with model generalizability than label-dependency
methods, they often generate hundreds of meaningless or noisy proposals with
non-discriminative information, and the contextual dependency among the
localized regions is often ignored or over-simplified. This paper builds a
unified framework to perform effective noisy-proposal suppression and to
interact between global and local features for robust feature learning.
Specifically, we propose category-aware weak supervision to concentrate on
non-existent categories so as to provide deterministic information for local
feature learning, restricting the local branch to focus on more high-quality
regions of interest. Moreover, we develop a cross-granularity attention module
to explore the complementary information between global and local features,
which can build the high-order feature correlation containing not only
global-to-local, but also local-to-local relations. Both advantages guarantee a
boost in the performance of the whole network. Extensive experiments on two
large-scale datasets (MS-COCO and VOC 2007) demonstrate that our framework
achieves superior performance over state-of-the-art methods.

该研究构建了一个统一框架，以执行有效的噪声提议抑制并相互作用全局和局部特征进行稳健的特征学习，包括使用类别感知弱监督来集中注意力于不存在的类别为本地特征学习提供确定性信息，以及开发跨粒度注意力模块来探索全局和局部特征之间的互补信息。在 MS-COCO 和 VOC 2007 两个大规模数据集上的广泛实验表明，该框架优于最先进的方法。