  Recognizing multiple labels of an image is a practical yet challenging task,
and remarkable progress has been achieved by searching for semantic regions and
exploiting label dependencies. However, current works utilize RNN/LSTM to
implicitly capture sequential region/label dependencies, which cannot fully
explore mutual interactions among the semantic regions/labels and do not
explicitly integrate label co-occurrences. In addition, these works require
large amounts of training samples for each category, and they are unable to
generalize to novel categories with limited samples. To address these issues,
we propose a knowledge-guided graph routing (KGGR) framework, which unifies
prior knowledge of statistical label correlations with deep neural networks.
The framework exploits prior knowledge to guide adaptive information
propagation among different categories to facilitate multi-label analysis and
reduce the dependency of training samples. Specifically, it first builds a
structured knowledge graph to correlate different labels based on statistical
label co-occurrence. Then, it introduces the label semantics to guide learning
semantic-specific features to initialize the graph, and it exploits a graph
propagation network to explore graph node interactions, enabling learning
contextualized image feature representations. Moreover, we initialize each
graph node with the classifier weights for the corresponding label and apply
another propagation network to transfer node messages through the graph. In
this way, it can facilitate exploiting the information of correlated labels to
help train better classifiers. We conduct extensive experiments on the
traditional multi-label image recognition (MLR) and multi-label few-shot
learning (ML-FSL) tasks and show that our KGGR framework outperforms the
current state-of-the-art methods by sizable margins on the public benchmarks.
