With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

Human beings experience life through a spectrum of modes such as vision, taste, hearing,
smell, and touch. These multiple modes are integrated for information processing in our …

In this paper, we propose a simple unsupervised graph representation learning method to
conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss …

Hashing methods for efficient image retrieval aim at learning hash functions that map similar
images to semantically correlated binary codes in the Hamming space with similarity well …

The task of image-text matching refers to measuring the visual-semantic similarity between
an image and a sentence. Recently, the fine-grained matching methods that explore the …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

The success of existing multi-view clustering relies on the assumption of sample integrity
across multiple views. However, in real-world scenarios, samples of multi-view are partially …

Hashing methods have been extensively applied to efficient multimedia data indexing and
retrieval on account of the explosion of multimedia data. Cross-modal hashing usually …

Incomplete multi-view clustering (IMVC) is an important unsupervised approach to group the
multi-view data containing missing data in some views. Previous IMVC methods suffer from …

With the development of deep learning technology, many deep learning methods have been
applied to font recognition and generation. However, few studies focus on font inpainting …