Dynamic deformable transformer for end-to-end face alignment

IF 1.5 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IET Computer Vision Pub Date : 2023-05-30 DOI:10.1049/cvi2.12208

Liming Han, Chi Yang, Qing Li, Bin Yao, Zixian Jiao, Qianyang Xie

引用次数: 0

Abstract

Heatmap-based regression (HBR) methods have dominated for a long time in the face alignment field while these methods need complex design and post-processing. In this study, the authors propose an end-to-end and simple enough coordinate-based regression (CBR) method called Dynamic Deformable Transformer (DDT) for face alignment. Unlike general pre-defined landmark queries, DDT uses Dynamic Landmark Queries (DLQs) to query landmarks' classes and coordinates together. Besides, DDT adopts a deformable attention mechanism rather than a regular attention mechanism which has a faster convergence speed and lower computational complexity. Experiment results on three mainstream datasets 300W, WFLW, and COFW demonstrate DDT exceeds the state-of-the-art CBR methods by a large margin and is comparable to the current state-of-the-art HBR methods with much less computational complexity.

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用于端对端面对齐的动态可变形变压器

基于热图的回归（HBR）方法在人脸配准领域长期占据主导地位，但这些方法需要复杂的设计和后处理。在这项研究中，作者提出了一种端到端且足够简单的基于坐标的回归（CBR）方法，称为动态可变形变换器（DDT），用于人脸配准。与一般的预定义地标查询不同，DDT 使用动态地标查询（DLQ）来同时查询地标的类别和坐标。此外，DDT 采用的是可变形关注机制而非普通关注机制，收敛速度更快，计算复杂度更低。在 300W、WFLW 和 COFW 三个主流数据集上的实验结果表明，DDT 远远超过了最先进的 CBR 方法，并可与目前最先进的 HBR 方法相媲美，而且计算复杂度更低。

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来源期刊

IET Computer Vision 工程技术-工程：电子与电气

CiteScore

3.30

自引率

11.80%

发文量

审稿时长

3.4 months

期刊介绍： IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision. IET Computer Vision welcomes submissions on the following topics: Biologically and perceptually motivated approaches to low level vision (feature detection, etc.); Perceptual grouping and organisation Representation, analysis and matching of 2D and 3D shape Shape-from-X Object recognition Image understanding Learning with visual inputs Motion analysis and object tracking Multiview scene analysis Cognitive approaches in low, mid and high level vision Control in visual systems Colour, reflectance and light Statistical and probabilistic models Face and gesture Surveillance Biometrics and security Robotics Vehicle guidance Automatic model aquisition Medical image analysis and understanding Aerial scene analysis and remote sensing Deep learning models in computer vision Both methodological and applications orientated papers are welcome. Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review. Special Issues Current Call for Papers: Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf