ActBERT

ActBERT is a sophisticated transformer-based framework designed for joint video-and-language representation learning. Developed as a research-first architecture (introduced by Baidu Research), it addresses the limitations of standard multimodal models by incorporating an 'action-aware' mechanism. The architecture utilizes a Tangent Visual Transformer to model global actions alongside local regional objects, creating a three-source input stream: global action features, local objects (regions of interest), and linguistic descriptions. By leveraging self-supervised learning on large-scale datasets like HowTo100M, ActBERT achieves state-of-the-art performance in cross-modal tasks. In the 2026 market, ActBERT remains a foundational architecture for developers building custom video-text retrieval systems, automated video captioning tools, and advanced video-based question-answering systems. Its ability to disentangle action-level information from object-level data allows for a more nuanced understanding of temporal dynamics in video, making it superior to static image-text models adapted for video. It is primarily deployed via PyTorch and serves as a backbone for highly specialized industrial AI applications in media, security, and accessibility.