Due to object detection's close relationship with video analysis and image understanding, it
has attracted much research attention in recent years. Traditional object detection methods …

Where previous reviews on content-based image retrieval emphasize what can be seen in
an image to bridge the semantic gap, this survey considers what people tag about an image …

With the rapid advancements of the text-to-image generative model, AI-generated images
(AGIs) have been widely applied to entertainment, education, social media, etc. However …

In this paper, we present a hypergraph neural networks (HGNN) framework for data
representation learning, which can encode high-order data correlation in a hypergraph …

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In
recent years, hypergraph learning has attracted increasing attention due to its flexibility and …

Recent progress on salient object detection (SOD) mostly benefits from the explosive
development of Convolutional Neural Networks (CNNs). However, much of the improvement …

Traditional1 salient object detection models often use hand-crafted features to formulate
contrast and various prior knowledge, and then combine them artificially. In this work, we …

K nearest neighbors (kNN) is an efficient lazy learning algorithm and has successfully been
developed in real applications. It is natural to scale the kNN method to the large scale …

Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning,
where the high-level and low-level features collaborate in locating salient objects and …

Hypergraphs, encoding structured interactions among any number of system units, have
recently proven a successful tool to describe many real-world biological and social …