Baidu's Video Foundation Technology Architecture and Key AI Techniques
This article presents an overview of Baidu's video foundation technology architecture, covering the video R&D platform, core AI techniques for video understanding, editing, surveillance, and general vision, and detailing innovations such as Attention‑Cluster networks, cross‑modality attention with graph convolution, GANs, super‑resolution, and adaptive encoding.
Guest: Wen Shilei, Baidu Video Understanding Technology Lead.
Introduction: Baidu has accumulated fundamental video technologies over recent years, applied widely in both consumer and enterprise scenarios.
1. Video R&D Platform: Baidu primarily uses the PaddlePaddle platform and PaddleCV, which integrate training, inference, model libraries, and development tools.
2. Video AI Technologies: Includes video understanding, editing, surveillance, and general vision.
Video Understanding: Covers semantic analysis, quality assessment, and retrieval, with applications in content analysis, quality judgment, and multi‑label classification.
Video Editing: Techniques such as segmentation/keypoint/AR, super‑resolution, adaptive coding, and GANs for tasks like portrait segmentation and bandwidth reduction.
Video Surveillance: Human/vehicle/object detection, tracking, and similarity measurement.
General Vision: Pre‑trained models, classification/detection/segmentation, and neural architecture search.
Attention‑Cluster Network: An attention‑based video classification model addressing frame redundancy, local discriminability, approximate disorder, and multi‑segment separability, with extensions like Channel Pyramid Attention Cluster and Temporal Pyramid Attention Cluster.
Cross‑Modality Attention + Graph Convolution: A solution for multi‑label classification that fuses text and visual features using semantic graphs.
GAN and Super‑Resolution: Discussion of GAN fundamentals, StarGAN for facial attribute editing, and the comparative performance of GAN versus traditional methods for video super‑resolution.
Adaptive Encoding: Content‑adaptive encoding that selects optimal compression parameters per shot to balance bandwidth and quality.
References to relevant papers are provided, and implementations are available on the PaddlePaddle platform.
End of presentation.
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