  Multi-label classification aims to recognize multiple objects or attributes
from images. However, it is challenging to learn from proper label graphs to
effectively characterize such inter-label correlations or dependencies. Current
methods often use the co-occurrence probability of labels based on the training
set as the adjacency matrix to model this correlation, which is greatly limited
by the dataset and affects the model's generalization ability. In this paper,
we propose a Graph Attention Transformer Network (GATN), a general framework
for multi-label image classification that can effectively mine complex
inter-label relationships. First, we use the cosine similarity based on the
label word embedding as the initial correlation matrix, which can represent
rich semantic information. Subsequently, we design the graph attention
transformer layer to transfer this adjacency matrix to adapt to the current
domain. Our extensive experiments have demonstrated that our proposed methods
can achieve state-of-the-art performance on three datasets.
