In this video, a detailed explanation is provided on how ViTPose utilizes the Vision Transformer (ViT) architecture for the task of 2D human pose estimation. The video discusses the architecture of ViTPose and delves into the techniques employed to achieve impressive performance on the MS COCO dataset. The focus is on showcasing the effectiveness of ViTPose in accurately estimating human poses in 2D space. Various aspects of ViTPose's design and its contributions to advancing the state-of-the-art in human pose estimation are explored in the video.
Paper link: [ Ссылка ]
Table of Content:
00:00 Introduction
00:12 Previous Attempts
02:20 ViTPose
07:02 Variants
07:26 Simplicity and Scalability
08:33 Pre-training
10:10 Input Resolution
11:31 Attention Type
14:53 Partially Finetuuning
16:02 Multi-dataset Training
16:19 Knowledge Distillation
21:11 Results
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