A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition

• URL: http://arxiv.org/abs/2403.14318v1
• Date: Thu, 21 Mar 2024 11:40:51 GMT
• Title: A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition
• Authors: Ali Ezati, Mohammadreza Dezyani, Rajib Rana, Roozbeh Rajabi, Ahmad Ayatollahi,
• Abstract summary: We introduce a lightweight attentional network incorporating multi-scale feature fusion (LANMSFF) to tackle these issues. We present two novel components, namely mass attention (MassAtt) and point wise feature selection (PWFS) blocks. Our proposed approach achieved results comparable to state-of-the-art methods in terms of parameter counts and robustness to pose variation.
• Score: 2.9581436761331017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional neural networks (CNNs) and their variations have shown effectiveness in facial expression recognition (FER). However, they face challenges when dealing with high computational complexity and multi-view head poses in real-world scenarios. We introduce a lightweight attentional network incorporating multi-scale feature fusion (LANMSFF) to tackle these issues. For the first challenge, we have carefully designed a lightweight fully convolutional network (FCN). We address the second challenge by presenting two novel components, namely mass attention (MassAtt) and point wise feature selection (PWFS) blocks. The MassAtt block simultaneously generates channel and spatial attention maps to recalibrate feature maps by emphasizing important features while suppressing irrelevant ones. On the other hand, the PWFS block employs a feature selection mechanism that discards less meaningful features prior to the fusion process. This mechanism distinguishes it from previous methods that directly fuse multi-scale features. Our proposed approach achieved results comparable to state-of-the-art methods in terms of parameter counts and robustness to pose variation, with accuracy rates of 90.77% on KDEF, 70.44% on FER-2013, and 86.96% on FERPlus datasets. The code for LANMSFF is available at https://github.com/AE-1129/LANMSFF.

Related papers

• Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing [5.3598912592106345]
  Deep learning has led to significant advances in bearing fault diagnosis (FD) We propose a novel FD model by integrating multiscale quaternion convolutional neural network (MQCNN), bidirectional gated recurrent unit (BiG), and cross self-attention feature fusion (CSAFF)
  arXiv  Detail & Related papers  (2024-05-25T07:55:02Z)
• Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching [30.272791354494373]
  We introduce affine-based local attention to model cross-view deformations. We also present selective fusion to merge local and global messages from cross attention.
  arXiv  Detail & Related papers  (2024-05-22T17:57:37Z)
• Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization [9.90146712189936]
  This paper presents a multi-receptive-field non-local network (MRFNLN) for image dehazing. It is designed as a multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) It outperforms recent state-of-the-art dehazing methods with less than 1.5 Million parameters.
  arXiv  Detail & Related papers  (2023-09-28T14:59:16Z)
• Mutual-Guided Dynamic Network for Image Fusion [51.615598671899335]
  We propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks.
  arXiv  Detail & Related papers  (2023-08-24T03:50:37Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition [23.814762073093153]
  We propose a pure transformer-based multi-task PAR network named PARFormer, which includes four modules. In the feature extraction module, we build a strong baseline for feature extraction, which achieves competitive results on several PAR benchmarks. In the viewpoint perception module, we explore the impact of viewpoints on pedestrian attributes, and propose a multi-view contrastive loss. In the attribute recognition module, we alleviate the negative-positive imbalance problem to generate the attribute predictions.
  arXiv  Detail & Related papers  (2023-04-14T16:27:56Z)
• Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution [90.16462805389943]
  We develop a spatially-adaptive feature modulation (SAFM) mechanism upon a vision transformer (ViT)-like block. Proposed method is $3times$ smaller than state-of-the-art efficient SR methods.
  arXiv  Detail & Related papers  (2023-02-27T14:19:31Z)
• A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation [68.10621089649486]
  We propose Attention Aggregation based Feature Pyramid Network (A2-FPN) to improve multi-scale feature learning. A2-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade.
  arXiv  Detail & Related papers  (2021-05-07T11:51:08Z)
• Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search [65.51181219410763]
  One-step methods have been developed to handle pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. We propose a decoupled and memory-reinforced network (DMRNet) to overcome these problems.
  arXiv  Detail & Related papers  (2021-02-22T06:19:45Z)
• Multi-Attention Based Ultra Lightweight Image Super-Resolution [9.819866781885446]
  We propose a Multi-Attentive Feature Fusion Super-Resolution Network (MAFFSRN) MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively.
  arXiv  Detail & Related papers  (2020-08-29T05:19:32Z)
• Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification [53.58763562421771]
  We propose a novel deep multi-task multi-label CNN, termed DMM-CNN, for effective Facial Attribute Classification (FAC) Specifically, DMM-CNN jointly optimize two closely-related tasks (i.e., facial landmark detection and FAC) to improve the performance of FAC by taking advantage of multi-task learning. Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training.
  arXiv  Detail & Related papers  (2020-02-10T12:34:16Z)

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.