  The simultaneous recognition of multiple objects in one image remains a
challenging task, spanning multiple events in the recognition field such as
various object scales, inconsistent appearances, and confused inter-class
relationships. Recent research efforts mainly resort to the statistic label
co-occurrences and linguistic word embedding to enhance the unclear semantics.
Different from these researches, in this paper, we propose a novel
Transformer-based Dual Relation learning framework, constructing complementary
relationships by exploring two aspects of correlation, i.e., structural
relation graph and semantic relation graph. The structural relation graph aims
to capture long-range correlations from object context, by developing a
cross-scale transformer-based architecture. The semantic graph dynamically
models the semantic meanings of image objects with explicit semantic-aware
constraints. In addition, we also incorporate the learnt structural
relationship into the semantic graph, constructing a joint relation graph for
robust representations. With the collaborative learning of these two effective
relation graphs, our approach achieves new state-of-the-art on two popular
multi-label recognition benchmarks, i.e., MS-COCO and VOC 2007 dataset.
