Reorganizing implicit feedback of users as a user-item interaction graph facilitates the
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …

Recently micro-videos have become more popular in social media platforms such as TikTok
and Instagram. Engagements in these platforms are facilitated by multi-modal …

Understanding user preference on item context is the key to acquire a high-quality
multimedia recommendation. Typically, the pre-existing features of items are derived from …

In this paper, we propose a dynamic graph modeling approach to learn spatial-temporal
representations for video summarization. Most existing video summarization methods extract …

Multi-label image classification is an important and challenging task in computer vision and
multimedia fields. Most of the recent works only capture the pair-wise dependencies among …

Multi-video summarization is an effective tool for users to browse multiple videos. In this
paper, multi-video summarization is formulated as a graph analysis problem and a dynamic …

Graph convolutional networks (GCNs) provide a promising way to extract the useful
information from graph-structured data. Most of the existing GCNs methods usually focus on …

We address the problem of unsupervised video summarization that automatically selects key
video clips. Most state-of-the-art approaches suffer from two issues:(1) they model video …

Compared to a single fixed camera, multiple moving cameras, eg, those worn by people,
can better capture the human interactive and group activities in a scene, by providing …

Outfit compatibility modeling, which aims to automatically evaluate the matching degree of
an outfit, has drawn great research attention. Regarding the comprehensive evaluation …