  Weakly supervised object localization (WSOL) strives to learn to localize
objects with only image-level supervision. Due to the local receptive fields
generated by convolution operations, previous CNN-based methods suffer from
partial activation issues, concentrating on the object's discriminative part
instead of the entire entity scope. Benefiting from the capability of the
self-attention mechanism to acquire long-range feature dependencies, Vision
Transformer has been recently applied to alleviate the local activation
drawbacks. However, since the transformer lacks the inductive localization bias
that are inherent in CNNs, it may cause a divergent activation problem
resulting in an uncertain distinction between foreground and background. In
this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via
a transformer to converge on the divergent activation. Specifically, we first
propose a local patch shuffle strategy to construct the image pairs, disrupting
local patches while guaranteeing global consistency. The paired images that
contain the common object in spatial are then fed into the Siamese network
encoder. We further design a semantic-constraint matching module, which aims to
mine the co-object part by matching the coarse class activation maps (CAMs)
extracted from the pair images, thus implicitly guiding and calibrating the
transformer network to alleviate the divergent activation. Extensive
experimental results conducted on two challenging benchmarks, including
CUB-200-2011 and ILSVRC datasets show that our method can achieve the new
state-of-the-art performance and outperform the previous method by a large
margin.
