Recently, significant progress has been made in masked image modeling to catch up to
masked language modeling. However, unlike words in NLP, the lack of semantic …

We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable
image classifier that integrates the power of deep learning and the interpretability of case …

Though significant progress has been achieved on fine-grained visual classification (FGVC),
severe overfitting still hinders model generalization. A recent study shows that hard samples …

With the rapid increase of human activities in cyberspace, various network intrusions are
tended to be frequent and hidden. Network intrusion detection (NID) has attracted more and …

In this paper, we propose a novel part-level feature extraction method to enhance the
discriminative ability of deep convolutional features for the task of fine-grained vehicle …

Despite the great success of the existing work in fine-grained visual categorization (FGVC),
there are still several unsolved challenges, eg, poor interpretation and vagueness …

Fine-grained hashing is a new topic in the field of hashing-based retrieval and has not been
well explored up to now. In this paper, we raise three key issues that fine-grained hashing …

At many construction sites, whether to wear a helmet is directly related to the safety of the
workers. Therefore, the detection of helmet use has become a crucial monitoring tool for …

Large-scale fine-grained image retrieval based hashing learning method has two main
problems. First, low dimension feature embedding can fasten the retrieval process but bring …

Semantic segmentation of parts of objects is a marginally explored and challenging task in
which multiple instances of objects and multiple parts within those objects must be …