Related papers: Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning

Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning

URL: http://arxiv.org/abs/2203.06541v1
Date: Sun, 13 Mar 2022 01:15:23 GMT
Title: Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning
Authors: Jiahao Xia and Weiwei qu and Wenjian Huang and Jianguo Zhang and Xi Wang and Min Xu
Abstract summary: We propose a Sparse Local Patch Transformer (S) for learning the inherent relation. The proposed method works at the state-of-the-art level with much less computational complexity.
Score: 11.150290581561725
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a local patch and aggregates them by an adaptive inherent relation based on the attention mechanism. The subpixel coordinate of each landmark is predicted independently based on the aggregated feature. Moreover, a coarse-to-fine framework is further introduced to incorporate with the SLPT, which enables the initial landmarks to gradually converge to the target facial landmarks using fine-grained features from dynamically resized local patches. Extensive experiments carried out on three popular benchmarks, including WFLW, 300W and COFW, demonstrate that the proposed method works at the state-of-the-art level with much less computational complexity by learning the inherent relation between facial landmarks. The code is available at the project website.

Related papers

Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
arXiv Detail & Related papers (2024-07-26T06:29:09Z)
Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching [0.0]
We propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures. To deal with the order and sign ambiguity of Laplacian eigenmaps, we introduce a new operator, called Coupled Laplacian. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes.
arXiv Detail & Related papers (2024-02-27T10:10:12Z)
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers [60.51925353387151]
We propose a novel module named Local Context Propagation (LCP) to exploit the message passing between neighboring local regions. We use the overlap points of adjacent local regions as intermediaries, then re-weight the features of these shared points from different local regions before passing them to the next layers. The proposed method is applicable to different tasks and outperforms various transformer-based methods in benchmarks including 3D shape classification and dense prediction tasks.
arXiv Detail & Related papers (2022-10-23T15:43:01Z)
RePFormer: Refinement Pyramid Transformer for Robust Facial Landmark Detection [131.1478251760399]
We formulate the facial landmark detection task as refining landmark queries along pyramid memories. Specifically, a pyramid transformer head (PTH) is introduced to build both relations among landmarks and heterologous relations between landmarks and cross-scale contexts. A dynamic landmark refinement (DLR) module is designed to decompose the landmark regression into an end-to-end refinement procedure.
arXiv Detail & Related papers (2022-07-08T14:12:26Z)
A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation [50.261472059743845]
Action Units (AUs) are geometrically-based atomic facial muscle movements. We propose a novel AU modelling problem that consists of jointly estimating their localisation and intensity. A Heatmap models whether an AU occurs or not at a given spatial location.
arXiv Detail & Related papers (2020-04-14T16:51:13Z)
Multi-View Optimization of Local Feature Geometry [70.18863787469805]
We address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry. Our proposed method naturally complements the traditional feature extraction and matching paradigm. We show that our method consistently improves the triangulation and camera localization performance for both hand-crafted and learned local features.
arXiv Detail & Related papers (2020-03-18T17:22:11Z)
A Rotation-Invariant Framework for Deep Point Cloud Analysis [132.91915346157018]
We introduce a new low-level purely rotation-invariant representation to replace common 3D Cartesian coordinates as the network inputs. Also, we present a network architecture to embed these representations into features, encoding local relations between points and their neighbors, and the global shape structure. We evaluate our method on multiple point cloud analysis tasks, including shape classification, part segmentation, and shape retrieval.
arXiv Detail & Related papers (2020-03-16T14:04:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.