Although significant progress has been made in few-shot learning, most of existing few-shot
image classification methods require supervised pre-training on a large amount of samples …

Document intelligence as a relatively new research topic supports many business
applications. Its main task is to automatically read, understand, and analyze documents …

Noisy correspondence that refers to mismatches in cross-modal data pairs is prevalent on
human-annotated or web-crawled datasets. Prior approaches to leverage such data mainly …

Identifying the target object in an image can produce more accurate and discriminative
feature representations, which can significantly improve large-scale instance-level image …

Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and
advance the field. Although previous reviews have primarily focused on binary and real …

This paper presents a novel method for supervised multi-view representation learning,
which projects multiple views into a latent common space while preserving the …

Information retrieval (IR) is a fundamental technique that aims to acquire information from a
collection of documents, web pages, or other sources. While traditional text-based IR has …

Temporal sentence grounding (TSG) aims to locate a semantically related segment of an
untrimmed video guided by a sentence query. Since the untrimmed videos are too long …

The core of semi-supervised cross-modal retrieval tasks lies in leveraging limited supervised
information to measure the similarity between cross-modal data. Current approaches …

Driven by the high nonlinearity of deep neural networks, deep hashing has achieved the
pictured great potential in cross-modal retrieval applications, significantly bridging the …