Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection

• URL: http://arxiv.org/abs/2303.08545v2
• Date: Sun, 19 Mar 2023 15:04:08 GMT
• Title: Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection
• Authors: Jun Yu, Renda Li, Zhongpeng Cai, Gongpeng Zhao, Guochen Xie, Jichao Zhu, Wangyuan Zhu
• Abstract summary: We introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) We propose a single-stage trained AU detection framework. Specifically, in order to effectively extract facial local region features related to AU detection, we use a local region perception module. We also use a graph neural network-based relational learning module to capture the relationship between AUs.
• Score: 12.677143408225167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human affective behavior analysis plays a vital role in human-computer interaction (HCI) systems. In this paper, we introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW). We propose a single-stage trained AU detection framework. Specifically, in order to effectively extract facial local region features related to AU detection, we use a local region perception module to effectively extract features of different AUs. Meanwhile, we use a graph neural network-based relational learning module to capture the relationship between AUs. In addition, considering the role of the overall feature of the target face on AU detection, we also use the feature fusion module to fuse the feature information extracted by the backbone network and the AU feature information extracted by the relationship learning module. We also adopted some sampling methods, data augmentation techniques and post-processing strategies to further improve the performance of the model.

Related papers

• Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection [37.841035367349434]
  Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks. This paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less proposes for AU detection.
  arXiv  Detail & Related papers  (2023-08-15T13:43:48Z)
• Self-supervised Facial Action Unit Detection with Region and Relation Learning [5.182661263082065]
  We propose a novel self-supervised framework for AU detection with the region and relation learning. An improved Optimal Transport (OT) algorithm is introduced to exploit the correlation characteristics among AUs. Swin Transformer is exploited to model the long-distance dependencies within each AU region during feature learning.
  arXiv  Detail & Related papers  (2023-03-10T05:22:45Z)
• Adaptive Local-Global Relational Network for Facial Action Units Recognition and Facial Paralysis Estimation [22.85506776477092]
  We propose a novel Adaptive Local-Global Network (ALGRNet) for facial AU recognition and apply it to facial paralysis estimation. ALGRNet consists of three novel structures, i.e., an adaptive region learning module which learns the adaptive muscle regions based on detected landmarks. Experiments on the BP4 and DISFA AU datasets show that the proposed approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-03-03T16:14:49Z)
• PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis [56.91758845045371]
  We propose a novel framework named Point Relation-Aware Network (PRA-Net) It is composed of an Intra-region Structure Learning (ISL) module and an Inter-region Relation Learning (IRL) module. Experiments on several 3D benchmarks covering shape classification, keypoint estimation, and part segmentation have verified the effectiveness and the ability of PRA-Net.
  arXiv  Detail & Related papers  (2021-12-09T13:24:43Z)
• An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning [77.72330187258498]
  We propose a novel Entropy-guided Reinforced Partial Convolutional Network (ERPCNet) ERPCNet extracts and aggregates localities based on semantic relevance and visual correlations without human-annotated regions. It not only discovers global-cooperative localities dynamically but also converges faster for policy gradient optimization.
  arXiv  Detail & Related papers  (2021-11-03T11:13:13Z)
• Kinship Verification Based on Cross-Generation Feature Interaction Learning [53.62256887837659]
  Kinship verification from facial images has been recognized as an emerging yet challenging technique in computer vision applications. We propose a novel cross-generation feature interaction learning (CFIL) framework for robust kinship verification.
  arXiv  Detail & Related papers  (2021-09-07T01:50:50Z)
• Action Unit Detection with Joint Adaptive Attention and Graph Relation [3.98807633060402]
  We present our submission to the Field Affective Behavior Analysis (ABAW) 2021 competition. The proposed method uses the pre-trained JAA model as the feature extractor. Our model achieves 0.674 on the challenging Aff-Wild2 database.
  arXiv  Detail & Related papers  (2021-07-09T12:33:38Z)
• Transforming Feature Space to Interpret Machine Learning Models [91.62936410696409]
  This contribution proposes a novel approach that interprets machine-learning models through the lens of feature space transformations. It can be used to enhance unconditional as well as conditional post-hoc diagnostic tools. A case study on remote-sensing landcover classification with 46 features is used to demonstrate the potential of the proposed approach.
  arXiv  Detail & Related papers  (2021-04-09T10:48:11Z)
• Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition [30.129452080084224]
  We propose a novel multi-level adaptive ROI and graph learning (MARGL) framework to tackle this problem. In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed. Experiments on BP4D and DISFA demonstrate the proposed MARGL significantly outperforms the previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-24T09:22:45Z)
• J$\hat{\text{A}}$A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention [57.51255553918323]
  We propose a novel end-to-end deep learning framework for joint AU detection and face alignment. Our framework significantly outperforms the state-of-the-art AU detection methods on the challenging BP4D, DISFA, GFT and BP4D+ benchmarks.
  arXiv  Detail & Related papers  (2020-03-18T12:50:19Z)
• Global Context-Aware Progressive Aggregation Network for Salient Object Detection [117.943116761278]
  We propose a novel network named GCPANet to integrate low-level appearance features, high-level semantic features, and global context features. We show that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-03-02T04:26:10Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.