Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09286 (cs)
[Submitted on 10 Mar 2026]
Title:CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation
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Abstract:Beyond conveying semantic information, an image can also manifest cognitive attributes that elicit specific cognitive processes from the viewer, such as memory encoding or emotional response. While modern text-to-image models excel at generating semantically coherent content, they remain limited in their ability to control such cognitive properties of images (e.g., valence, memorability), often failing to align with the specific psychological intent. To bridge this gap, we introduce CogBlender, a framework that enables continuous and multi-dimensional intervention of cognitive properties during text-to-image generation. Our approach is built upon a mapping between the Cognitive Space, representing the space of cognitive properties, and the Semantic Manifold, representing the manifold of the visual semantics. We define a set of Cognitive Anchors, serving as the boundary points for the cognitive space. Then we reformulate the velocity field within the flow-matching process by interpolating from the velocity field of different anchors. Consequently, the generative process is driven by the velocity field and dynamically steered by multi-dimensional cognitive scores, enabling precise, fine-grained, and continuous intervention. We validate the effectiveness of CogBlender across four representative cognitive dimensions: valence, arousal, dominance, and image memorability. Extensive experiments demonstrate that our method achieves effective cognitive intervention. Our work provides an effective paradigm for cognition-driven creative design.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09286 [cs.CV] |
| (or arXiv:2603.09286v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09286
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View a PDF of the paper titled CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation, by Shengqi Dang and 4 other authors
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