Wang, Y., Wang, J., Fan, Y., Chai, Q., Zhang, H., Li, X., & Li, R. (2025). Multi-granularity contrastive zero-shot learning model based on attribute decomposition. Information Processing & Management, 62(1), 103898. https://doi.org/10.1016/j.ipm.2024.103898
Abstract:
Zero-shot learning aims to identify new classes by transferring semantic knowledge from seen
classes to unseen classes. Image classification based on zero-shot learning usually regards
different attributes as the carriers of semantic knowledge, for different attributes have different
effects on the task. However, existing models lack a differentiated understanding of different
attributes and ignore the impact of global context information. Therefore, this paper proposes
a multi-granularity contrastive zero-shot learning model based on attribute decomposition.
Specifically, different attributes are initially divided into key and common attributes i.e., attribute
decomposition. Then, inspired by Navon’s global-local paradigm, we work out the multigranularity
contrastive learning model, which is composed of the global learning module and the
local one, to further enhance the interaction between the global and local information. Finally,
zero-shot image classification is achieved by training a multi-granularity contrastive learning
model. The method is experimented on three public zero-shot learning benchmark data sets (i.e.,
AWA2, CUB and SUN). Compared with the existing model, in the CUB dataset, the harmonic
mean H is improved by 1.6%; in the SUN dataset, H increased by 7.1%; in the AWA2 dataset, H
increased by 2.5%, further verifying the effectiveness of this model.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Science and Technology Major Project - NA
Grant Reference no. : 2020AAA0106102
This research / project is supported by the the National Natural Science Foundation of China - NA
Grant Reference no. : 62176145