Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation

• URL: http://arxiv.org/abs/2412.13577v1
• Date: Wed, 18 Dec 2024 07:51:35 GMT
• Title: Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation
• Authors: Jiankun Zhu, Sicheng Zhao, Jing Jiang, Wenbo Tang, Zhaopan Xu, Tingting Han, Pengfei Xu, Hongxun Yao,
• Abstract summary: Visual emotion recognition (VER) aims at understanding humans' emotional reactions toward different visual stimuli. domain adaptation offers an alternative solution by adapting models trained on labeled source data to unlabeled target data. Due to privacy concerns, source emotional data may be inaccessible. We propose a novel framework termed Bridge then Begin Anew (BBA), which consists of two steps: domain-bridged model generation (DMG) and target-related model adaptation (TMA)
• Score: 22.638915084704344
• License:
• Abstract: Visual emotion recognition (VER), which aims at understanding humans' emotional reactions toward different visual stimuli, has attracted increasing attention. Given the subjective and ambiguous characteristics of emotion, annotating a reliable large-scale dataset is hard. For reducing reliance on data labeling, domain adaptation offers an alternative solution by adapting models trained on labeled source data to unlabeled target data. Conventional domain adaptation methods require access to source data. However, due to privacy concerns, source emotional data may be inaccessible. To address this issue, we propose an unexplored task: source-free domain adaptation (SFDA) for VER, which does not have access to source data during the adaptation process. To achieve this, we propose a novel framework termed Bridge then Begin Anew (BBA), which consists of two steps: domain-bridged model generation (DMG) and target-related model adaptation (TMA). First, the DMG bridges cross-domain gaps by generating an intermediate model, avoiding direct alignment between two VER datasets with significant differences. Then, the TMA begins training the target model anew to fit the target structure, avoiding the influence of source-specific knowledge. Extensive experiments are conducted on six SFDA settings for VER. The results demonstrate the effectiveness of BBA, which achieves remarkable performance gains compared with state-of-the-art SFDA methods and outperforms representative unsupervised domain adaptation approaches.

Related papers

• Subject-Based Domain Adaptation for Facial Expression Recognition [51.10374151948157]
  Adapting a deep learning model to a specific target individual is a challenging facial expression recognition task. This paper introduces a new MSDA method for subject-based domain adaptation in FER. It efficiently leverages information from multiple source subjects to adapt a deep FER model to a single target individual.
  arXiv  Detail & Related papers  (2023-12-09T18:40:37Z)
• Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation [84.82153655786183]
  We propose a novel framework called Informative Data Mining (IDM) to enable efficient one-shot domain adaptation for semantic segmentation. IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7%/55.4% on the GTA5/SYNTHIA to Cityscapes adaptation tasks.
  arXiv  Detail & Related papers  (2023-09-25T15:56:01Z)
• Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection [79.89082006155135]
  Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. UDA methods try to align the source and target representations to improve the generalization on the target domain. The Source-Free Adaptation Domain (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
  arXiv  Detail & Related papers  (2022-03-29T17:50:43Z)
• Transformer-Based Source-Free Domain Adaptation [134.67078085569017]
  We study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. We propose a generic and effective framework based on Transformer, named TransDA, for learning a generalized model for SFDA.
  arXiv  Detail & Related papers  (2021-05-28T23:06:26Z)
• Distill and Fine-tune: Effective Adaptation from a Black-box Source Model [138.12678159620248]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge in previous related labeled datasets (source) to a new unlabeled dataset (target) We propose a novel two-step adaptation framework called Distill and Fine-tune (Dis-tune)
  arXiv  Detail & Related papers  (2021-04-04T05:29:05Z)
• Source-Free Domain Adaptation for Semantic Segmentation [11.722728148523366]
  Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network-based approaches for semantic segmentation heavily rely on the pixel-level annotated data. We propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation.
  arXiv  Detail & Related papers  (2021-03-30T14:14:29Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.