This article presents a comprehensive multi-modal video understanding system for AIGC video generation, detailing technical architecture, GCN-based semi-supervised learning, and practical applications across automotive content scenarios.
This paper introduces a method for video multi-label classification that incorporates label correlation features using graph convolutional networks, significantly improving classification performance.
The paper proposes a video recommendation framework that combines recall and ranking modules, using a multi‑modal topic clustering approach—integrating audio, visual, and textual features via NeXtVLAD, PCA, and K‑Means—to generate unified video representations, improve candidate selection, and boost click‑through and viewing time, while addressing cold‑start and semantic relevance challenges.
