  In this paper, we propose a method to identify identical commodities. In
e-commerce scenarios, commodities are usually described by both images and
text. By definition, identical commodities are those that have identical key
attributes and are cognitively identical to consumers. There are two main
challenges: 1) The extraction and fusion of multi-modal representation. 2) The
ability to verify identical commodities by comparing the similarity between
representations and a threshold. To address the above problems, we propose an
end-to-end multi-modal representation learning method with self-adaptive
threshold. We use a dual-stream network to extract multi-modal commodity
embeddings and threshold embeddings separately and then concatenate them to
obtain commodity representation. Our method is able to adaptively adjust the
threshold according to different commodities while maintaining the indexability
of the commodity representation space. We experimentally validate the
advantages of self-adaptive threshold and the effectiveness of multimodal
representation fusion. Besides, our method achieves third place with an F1
score of 0.8936 on the second task of the CCKS-2022 Knowledge Graph Evaluation
for Digital Commerce Competition. Code and pretrained models are available at
https://github.com/hanchenchen/CCKS2022-track2-solution.
