Regressor-Guided Image Editing Regulates Emotional Response to Reduce Online Engagement

• URL: http://arxiv.org/abs/2501.12289v1
• Date: Tue, 21 Jan 2025 16:59:13 GMT
• Title: Regressor-Guided Image Editing Regulates Emotional Response to Reduce Online Engagement
• Authors: Christoph Gebhardt, Robin Willardt, Seyedmorteza Sadat, Chih-Wei Ning, Andreas Brombach, Jie Song, Otmar Hilliges, Christian Holz,
• Abstract summary: We propose three regressor-guided image editing approaches aimed at diminishing the emotional impact of images.<n>Our findings demonstrate that approaches can effectively alter the emotional properties of images while maintaining high visual quality.<n>Results from a behavioral study reveal that only the diffusion-based approach successfully elicits changes in viewers' emotional responses.
• Score: 40.65885791860718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotions are known to mediate the relationship between users' content consumption and their online engagement, with heightened emotional intensity leading to increased engagement. Building on this insight, we propose three regressor-guided image editing approaches aimed at diminishing the emotional impact of images. These include (i) a parameter optimization approach based on global image transformations known to influence emotions, (ii) an optimization approach targeting the style latent space of a generative adversarial network, and (iii) a diffusion-based approach employing classifier guidance and classifier-free guidance. Our findings demonstrate that approaches can effectively alter the emotional properties of images while maintaining high visual quality. Optimization-based methods primarily adjust low-level properties like color hues and brightness, whereas the diffusion-based approach introduces semantic changes, such as altering appearance or facial expressions. Notably, results from a behavioral study reveal that only the diffusion-based approach successfully elicits changes in viewers' emotional responses while preserving high perceived image quality. In future work, we will investigate the impact of these image adaptations on internet user behavior.

Related papers

• From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition [7.362433184546492]
  Dynamic Facial Expression Recognition aims to identify human emotions from temporally evolving facial movements.<n>Our method integrates dynamic motion modeling, semantic text refinement, and token-level cross-modal alignment to facilitate the precise localization of emotionally salient features.
  arXiv  Detail & Related papers  (2025-07-16T04:15:06Z)
• Visually grounded emotion regulation via diffusion models and user-driven reappraisal [0.0]
  We propose a novel, visually based augmentation of cognitive reappraisal by integrating large-scale text-to-image diffusion models into the emotional regulation process.<n>Specifically, we introduce a system in which users reinterpret emotionally negative images via spoken reappraisals.<n>This generative transformation visually instantiates users' reappraisals while maintaining structural similarity to the original stimuli, externalizing and reinforcing regulatory intent.
  arXiv  Detail & Related papers  (2025-07-14T23:28:59Z)
• Affective Image Editing: Shaping Emotional Factors via Text Descriptions [46.13506671212571]
  We introduce AIEdiT for Affective Image Editing using Text descriptions.<n>We build the continuous emotional spectrum and extract nuanced emotional requests.<n>AIEdiT achieves superior performance, effectively reflecting users' emotional requests.
  arXiv  Detail & Related papers  (2025-05-24T13:46:57Z)
• Continuous Adversarial Text Representation Learning for Affective Recognition [1.319058156672392]
  We propose a novel framework for enhancing emotion-aware embeddings in transformer-based models. Our approach introduces a continuous valence-arousal labeling system to guide contrastive learning. We employ a dynamic token perturbation mechanism, using gradient-based saliency to focus on sentiment-relevant tokens, improving model sensitivity to emotional cues.
  arXiv  Detail & Related papers  (2025-02-28T00:29:09Z)
• HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning [62.264673293638175]
  Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications.<n>We propose a invertible network for underwater perception enhancement, dubbed H, which enhances visual quality and demonstrates flexibility in handling other downstream tasks.
  arXiv  Detail & Related papers  (2024-11-27T12:37:03Z)
• Data Augmentation via Latent Diffusion for Saliency Prediction [67.88936624546076]
  Saliency prediction models are constrained by the limited diversity and quantity of labeled data. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
  arXiv  Detail & Related papers  (2024-09-11T14:36:24Z)
• Neural Additive Image Model: Interpretation through Interpolation [0.0]
  We propose a holistic modeling approach utilizing Neural Additive Models and Diffusion Autoencoders. We demonstrate that the proposed method can precisely identify complex image effects in an ablation study. To further showcase the practical applicability of our proposed model, we conduct a case study in which we investigate how the distinctive features and attributes captured within host images exert influence on the pricing of Airbnb rentals.
  arXiv  Detail & Related papers  (2024-03-06T16:46:07Z)
• EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models [11.901294654242376]
  We introduce Emotional Image Content Generation (EICG), a new task to generate semantic-clear and emotion-faithful images given emotion categories. Specifically, we propose an emotion space and construct a mapping network to align it with the powerful Contrastive Language-Image Pre-training (CLIP) space. Our method outperforms the state-of-the-art text-to-image approaches both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2024-01-09T15:23:21Z)
• Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
  We present Impressions, a novel dataset through which to investigate the semiotics of images. We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images. This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
  arXiv  Detail & Related papers  (2023-10-27T04:30:18Z)
• StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning [69.06749934902464]
  We propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL. StyleEDL interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations.
  arXiv  Detail & Related papers  (2023-08-06T03:22:46Z)
• Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition [29.916609743097215]
  Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references. In this work, we regard the emotion elicitation approach as domain knowledge, and explore domain transfer learning techniques on emotional utterances.
  arXiv  Detail & Related papers  (2021-04-05T15:59:31Z)
• Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation [136.53288628437355]
  Controllable semantic image editing enables a user to change entire image attributes with few clicks. Current approaches often suffer from attribute edits that are entangled, global image identity changes, and diminished photo-realism. We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work which primarily focuses on qualitative evaluation.
  arXiv  Detail & Related papers  (2021-02-01T21:38:36Z)
• Disentangle Perceptual Learning through Online Contrastive Learning [16.534353501066203]
  Pursuing realistic results according to human visual perception is the central concern in the image transformation tasks. In this paper, we argue that, among the features representation from the pre-trained classification network, only limited dimensions are related to human visual perception. Under such an assumption, we try to disentangle the perception-relevant dimensions from the representation through our proposed online contrastive learning.
  arXiv  Detail & Related papers  (2020-06-24T06:48:38Z)

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.