TransFER: Learning Relation-aware Facial Expression Representations with Transformers

• URL: http://arxiv.org/abs/2108.11116v1
• Date: Wed, 25 Aug 2021 08:28:34 GMT
• Title: TransFER: Learning Relation-aware Facial Expression Representations with Transformers
• Abstract summary: We propose the TransFER model which can learn rich relation-aware local representations. It mainly consists of three components: Multi-Attention Dropping (MAD), ViT-FER, and Multi-head Self-Attention Dropping (MSAD)
• Score: 28.168810464145313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial expression recognition (FER) has received increasing interest in computer vision. We propose the TransFER model which can learn rich relation-aware local representations. It mainly consists of three components: Multi-Attention Dropping (MAD), ViT-FER, and Multi-head Self-Attention Dropping (MSAD). First, local patches play an important role in distinguishing various expressions, however, few existing works can locate discriminative and diverse local patches. This can cause serious problems when some patches are invisible due to pose variations or viewpoint changes. To address this issue, the MAD is proposed to randomly drop an attention map. Consequently, models are pushed to explore diverse local patches adaptively. Second, to build rich relations between different local patches, the Vision Transformers (ViT) are used in FER, called ViT-FER. Since the global scope is used to reinforce each local patch, a better representation is obtained to boost the FER performance. Thirdly, the multi-head self-attention allows ViT to jointly attend to features from different information subspaces at different positions. Given no explicit guidance, however, multiple self-attentions may extract similar relations. To address this, the MSAD is proposed to randomly drop one self-attention module. As a result, models are forced to learn rich relations among diverse local patches. Our proposed TransFER model outperforms the state-of-the-art methods on several FER benchmarks, showing its effectiveness and usefulness.

Related papers

• When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models [81.7618160628979]
  Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored.<n>We introduce UPA-RFAS (Universal Patch Attack via Robust Feature, Attention, and Semantics), a unified framework that learns a single physical patch in a shared feature space.<n> Experiments across diverse VLA models, manipulation suites, and physical executions show that UPA-RFAS consistently transfers across models, tasks, and viewpoints.
  arXiv  Detail & Related papers  (2025-11-26T09:16:32Z)
• Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement [17.496082209866923]
  We refine two key modules of ViTs: attention maps and token embeddings. For attention maps, we propose Attention Map Diversification (AMD), which diversifies certain attention maps and also implicitly imposes beneficial vanishing during backward propagation. We conduct extensive experiments with adversarial examples transferred from ViTs to various CNNs and ViTs, demonstrating our FPR outperforms the current best (backward) surrogate refinement by up to 7.0% on average.
  arXiv  Detail & Related papers  (2025-03-19T16:44:23Z)
• Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification [11.664820595258988]
  Primary challenges in visible-infrared person re-identification arise from the differences between visible (vis) and infrared (ir) images. Existing methods often rely on horizontal partitioning to align part-level features, which can introduce inaccuracies. We propose a novel Prototype-Driven Multi-feature generation framework (PDM) aimed at mitigating cross-modal discrepancies.
  arXiv  Detail & Related papers  (2024-09-09T14:12:23Z)
• PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification [73.64560354556498]
  Vision Transformer (ViT) tends to overfit on most distinct regions of training data, limiting its generalizability and attention to holistic object features. We present PartFormer, an innovative adaptation of ViT designed to overcome the limitations in object Re-ID tasks. Our framework significantly outperforms state-of-the-art by 2.4% mAP scores on the most challenging MSMT17 dataset.
  arXiv  Detail & Related papers  (2024-08-29T16:31:05Z)
• Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification [64.36210786350568]
  We propose a novel learning framework named textbfEDITOR to select diverse tokens from vision Transformers for multi-modal object ReID. Our framework can generate more discriminative features for multi-modal object ReID.
  arXiv  Detail & Related papers  (2024-03-15T12:44:35Z)
• M2Former: Multi-Scale Patch Selection for Fine-Grained Visual Recognition [4.621578854541836]
  We propose multi-scale patch selection (MSPS) to improve the multi-scale capabilities of existing ViT-based models. Specifically, MSPS selects salient patches of different scales at different stages of a vision Transformer (MS-ViT) In addition, we introduce class token transfer (CTT) and multi-scale cross-attention (MSCA) to model cross-scale interactions between selected multi-scale patches and fully reflect them in model decisions.
  arXiv  Detail & Related papers  (2023-08-04T06:41:35Z)
• Siamese DETR [87.45960774877798]
  We present Siamese DETR, a self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-invariant and detection-oriented representations simultaneously through two complementary tasks. The proposed Siamese DETR achieves state-of-the-art transfer performance on COCO and PASCAL VOC detection.
  arXiv  Detail & Related papers  (2023-03-31T15:29:25Z)
• FER-former: Multi-modal Transformer for Facial Expression Recognition [14.219492977523682]
  A novel multifarious supervision-steering Transformer for Facial Expression Recognition is proposed in this paper. Our approach features multi-granularity embedding integration, hybrid self-attention scheme, and heterogeneous domain-steering supervision. Experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-arts.
  arXiv  Detail & Related papers  (2023-03-23T02:29:53Z)
• Patch-level Representation Learning for Self-supervised Vision Transformers [68.8862419248863]
  Vision Transformers (ViTs) have gained much attention recently as a better architectural choice, often outperforming convolutional networks for various visual tasks. Inspired by this, we design a simple yet effective visual pretext task, coined SelfPatch, for learning better patch-level representations. We demonstrate that SelfPatch can significantly improve the performance of existing SSL methods for various visual tasks.
  arXiv  Detail & Related papers  (2022-06-16T08:01:19Z)
• Self-Promoted Supervision for Few-Shot Transformer [178.52948452353834]
  Self-promoted sUpervisioN (SUN) is a few-shot learning framework for vision transformers (ViTs) SUN pretrains the ViT on the few-shot learning dataset and then uses it to generate individual location-specific supervision for guiding each patch token. Experiments show that SUN using ViTs significantly surpasses other few-shot learning frameworks with ViTs and is the first one that achieves higher performance than those CNN state-of-the-arts.
  arXiv  Detail & Related papers  (2022-03-14T12:53:27Z)
• Boosting Crowd Counting via Multifaceted Attention [109.89185492364386]
  Large-scale variations often exist within crowd images. Neither fixed-size convolution kernel of CNN nor fixed-size attention of recent vision transformers can handle this kind of variation. We propose a Multifaceted Attention Network (MAN) to improve transformer models in local spatial relation encoding.
  arXiv  Detail & Related papers  (2022-03-05T01:36:43Z)
• Robust Facial Expression Recognition with Convolutional Visual Transformers [23.05378099875569]
  We propose Convolutional Visual Transformers to tackle Facial Expression Recognition in the wild by two main steps. First, we propose an attentional selective fusion (ASF) for leveraging the feature maps generated by two-branch CNNs. Second, inspired by the success of Transformers in natural language processing, we propose to model relationships between these visual words with global self-attention.
  arXiv  Detail & Related papers  (2021-03-31T07:07:56Z)
• TransFG: A Transformer Architecture for Fine-grained Recognition [27.76159820385425]
  Recently, vision transformer (ViT) shows its strong performance in the traditional classification task. We propose a novel transformer-based framework TransFG where we integrate all raw attention weights of the transformer into an attention map. A contrastive loss is applied to further enlarge the distance between feature representations of similar sub-classes.
  arXiv  Detail & Related papers  (2021-03-14T17:03:53Z)

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.