Neural Information Processing Systems http://nips.cc/

We explore those different aspects in relation to mutual knowledge, and analyze zero-shot predictions.

To close such semantic gap, we propose Concept Curation (CoCu), a pipeline that leverages CLIP to compensate for the missing semantics.

Neural Information Processing Systems http://nips.cc/

Table 2: The accuracy on the VQA v2.0 test set. We thank all the reviewers for the helpful comments. Q1: How the paper's contribution relates to the current SOT A? SGAE is a rather complicated scene-graph based method specific to image captioning. The results with current SOT A + MIA will be stated more clearly in the paper. Q2: How to use MIA on the baseline systems (i.e., how is MIA applied to image captioning For the settings, we have listed them in the supplementary materials.

Blending visual and textual concepts into a new visual concept is a unique and powerful trait of human beings that can fuel creativity. However, in practice, cross-modal conceptual blending for humans is prone to cognitive biases, like design fixation, which leads to local minima in the design space. In this paper, we propose a T2I diffusion adapter "IT-Blender" that can automate the blending process to enhance human creativity. Prior works related to cross-modal conceptual blending are limited in encoding a real image without loss of details or in disentangling the image and text inputs. To address these gaps, IT-Blender leverages pretrained diffusion models (SD and FLUX) to blend the latent representations of a clean reference image with those of the noisy generated image. Combined with our novel blended attention, IT-Blender encodes the real reference image without loss of details and blends the visual concept with the object specified by the text in a disentangled way. Our experiment results show that IT-Blender outperforms the baselines by a large margin in blending visual and textual concepts, shedding light on the new application of image generative models to augment human creativity.