IECLA: Interpretable Emotion Contribution Learning for Affective Image Editing
Affective image editing (AIE) aims to evoke a target emotion while preserving the content and structure of a source image. Existing methods improve emotional expression or visual fidelity, but the target emotion alone does not reveal which source regions should be changed and which should remain intact, especially in complex scenes. We introduce Interpretable Emotion Contribution Learning for Affective Image Editing (IECLA), a framework that separates source-emotion attribution from target-aware editing. IECLA constructs signed object-level supervision through counterfactual interventions and trains a context-conditioned contribution predictor to localize evidence that supports or opposes...
| Authors | Yijie Guo, Dexiang Hong, Weidong Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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