The paper "Forging Emotions" investigates how Generative Adversarial Networks (GANs) can be used to generate images that convey or trigger specific emotions. It highlights that current datasets for high‑level concepts like emotions are inadequate and suggests incorporating psychological emotional networks into dataset construction. The study also finds that simple color cues are insufficient for affect generation, advocating for deeper analysis of GAN structure and composition to create genuinely emotive imagery.
arXiv:2609.24215v1 Announce Type: new Abstract: Although text-to-image models can accurately depict subjects and scenes, creators still struggle to specify the fine-grained emotions an image should c...
The paper introduces Visual‑Semantic Affect Graphs (VSAG), a graph‑based framework that models artworks using interpretable feature nodes such as color, brushstroke, composition, and visual entities. VSAG learns feature‑emotion associations and optionally incorporates a hierarchical valence‑arousal module to predict dominant emotions. Experiments on EmoArt‑5k show that VSAG, especially its valence‑arousal variant, outperforms direct vision‑language model inference and improves accuracy, macro‑F1, and weighted‑F1 scores, indicating that explicit visual‑semantic structure enhances both prediction and interpretation of artwork emotions.
arXiv:2606. 13081v1 Announce Type: cross Abstract: Emotion significantly influences cognition, enhancing memory and learning under certain conditions.
arXiv:2607. 10678v1 Announce Type: new Abstract: Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states.
arXiv:2607. 10165v1 Announce Type: cross Abstract: Emotion-aware artistic image generation requires an image to match the input prompt, follow the specified artistic style, and convey the target emotion.
AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source state and a signed difference vector to encode the desired change, which is then translated into context‑dependent semantic and appearance modifications via a transition encoder and a diffusion backbone. The authors introduce AffectPair‑249K, a dataset of 248,841 source‑target pairs covering both cross‑category and within‑category transitions, and show that AffectDelta outperforms six baselines in affective alignment and content preservation.
AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source image’s affective state and encodes the signed difference to guide a diffusion backbone that applies context‑dependent semantic and appearance changes. The authors created a large AffectPair‑249K dataset of source‑target pairs and show that AffectDelta outperforms six baselines in both affective alignment and content preservation, with ablation studies supporting their design choices.
The paper explores how AI can develop its own aesthetic categorization of art across text, audio, image, and video without explicit labels. Using a self‑supervised framework, the authors embed these modalities into a shared 256‑dimensional space and iteratively cluster the data to uncover aesthetic structure. They compare the AI’s cluster assignments with human affective labels, highlighting divergences and discussing implications for cross‑modal similarity, media organization, and automated labeling.
arXiv:2606. 28769v1 Announce Type: new Abstract: Emotional body motion expressions are an essential element of non-verbal communication.
arXiv:2606. 13247v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved impressive results in synthesizing high-quality images from natural language prompts.
The paper introduces a self‑supervised framework that maps text, audio, image, and video into a shared 256‑dimensional embedding space and uses iterative clustering to uncover aesthetic structure. It examines how AI’s cluster assignments diverge from human affective labels on a weakly supervised multimodal dataset. The study highlights implications for cross‑modal similarity, media organization for Retrieval‑Augmented Generation, and automated data labeling.
arXiv:2412.00176v4 Announce Type: replace Abstract: We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investi...