  We explore multiple-instance verification, a problem setting where a query
instance is verified against a bag of target instances with heterogeneous,
unknown relevancy. We show that naive adaptations of attention-based multiple
instance learning (MIL) methods and standard verification methods like Siamese
neural networks are unsuitable for this setting: directly combining
state-of-the-art (SOTA) MIL methods and Siamese networks is shown to be no
better, and sometimes significantly worse, than a simple baseline model.
Postulating that this may be caused by the failure of the representation of the
target bag to incorporate the query instance, we introduce a new pooling
approach named ``cross-attention pooling'' (CAP). Under the CAP framework, we
propose two novel attention functions to address the challenge of
distinguishing between highly similar instances in a target bag. Through
empirical studies on three different verification tasks, we demonstrate that
CAP outperforms adaptations of SOTA MIL methods and the baseline by substantial
margins, in terms of both classification accuracy and quality of the
explanations provided for the classifications. Ablation studies confirm the
superior ability of the new attention functions to identify key instances.
