Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training

• URL: http://arxiv.org/abs/2407.04963v1
• Date: Sat, 6 Jul 2024 05:29:02 GMT
• Title: Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training
• Authors: Dingkang Yang, Kun Yang, Haopeng Kuang, Zhaoyu Chen, Yuzheng Wang, Lihua Zhang,
• Abstract summary: Context-Aware Emotion Recognition (CAER) provides valuable semantic cues for recognizing the emotions of target persons. Current approaches invariably focus on designing sophisticated structures to extract perceptually critical representations from contexts. We present a Contextual Causal Intervention Module (CCIM) to de-confound the confounder.
• Score: 14.450673163785094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding emotions from diverse contexts has received widespread attention in computer vision communities. The core philosophy of Context-Aware Emotion Recognition (CAER) is to provide valuable semantic cues for recognizing the emotions of target persons by leveraging rich contextual information. Current approaches invariably focus on designing sophisticated structures to extract perceptually critical representations from contexts. Nevertheless, a long-neglected dilemma is that a severe context bias in existing datasets results in an unbalanced distribution of emotional states among different contexts, causing biased visual representation learning. From a causal demystification perspective, the harmful bias is identified as a confounder that misleads existing models to learn spurious correlations based on likelihood estimation, limiting the models' performance. To address the issue, we embrace causal inference to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a customized causal graph. Subsequently, we present a Contextual Causal Intervention Module (CCIM) to de-confound the confounder, which is built upon backdoor adjustment theory to facilitate seeking approximate causal effects during model training. As a plug-and-play component, CCIM can easily integrate with existing approaches and bring significant improvements. Systematic experiments on three datasets demonstrate the effectiveness of our CCIM.

Related papers

• Robust Emotion Recognition in Context Debiasing [12.487614699507793]
  Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Despite advancements, the biggest challenge remains due to context bias interference. We propose a counterfactual emotion inference (CLEF) framework to address the above issue.
  arXiv  Detail & Related papers  (2024-03-09T17:05:43Z)
• Towards Multimodal Human Intention Understanding Debiasing via Subject-Deconfounding [15.525357031558753]
  We propose SuCI, a causal intervention module to disentangle the impact of subjects acting as unobserved confounders. As a plug-and-play component, SuCI can be widely applied to most methods that seek unbiased predictions.
  arXiv  Detail & Related papers  (2024-03-08T04:03:54Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Interpretable Imitation Learning with Dynamic Causal Relations [65.18456572421702]
  We propose to expose captured knowledge in the form of a directed acyclic causal graph. We also design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-30T20:59:42Z)
• Context De-confounded Emotion Recognition [12.037240778629346]
  Context-Aware Emotion Recognition (CAER) aims to perceive the emotional states of the target person with contextual information. A long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states. This paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task.
  arXiv  Detail & Related papers  (2023-03-21T15:12:20Z)
• Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem [60.0878532426877]
  We propose a novel collaborative learning scheme from the viewpoint of visual perturbation calibration. Specifically, we devise a visual controller to construct two sorts of curated images with different perturbation extents. The experimental results on two diagnostic VQA-CP benchmark datasets evidently demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2022-07-24T23:50:52Z)
• Generalizable Information Theoretic Causal Representation [37.54158138447033]
  We propose to learn causal representation from observational data by regularizing the learning procedure with mutual information measures according to our hypothetical causal graph. The optimization involves a counterfactual loss, based on which we deduce a theoretical guarantee that the causality-inspired learning is with reduced sample complexity and better generalization ability.
  arXiv  Detail & Related papers  (2022-02-17T00:38:35Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)
• Causal Discovery in Physical Systems from Videos [123.79211190669821]
  Causal discovery is at the core of human cognition. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
  arXiv  Detail & Related papers  (2020-07-01T17:29:57Z)

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.