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Found 5 papers, 1 papers with code
Design-o-meter: Towards Evaluating and Refining Graphic Designs
To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting.
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models
To address this gap in the current literature, we propose our method called TINTIN: Test-time Conditional Text-to-Image Synthesis using Diffusion Models which is a new training-free test-time only algorithm to condition text-to-image diffusion model outputs on conditioning factors such as color palettes and edge maps.
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis
Another line of techniques expand the inversion space to learn multiple embeddings but they do this only along the layer dimension (e. g., one per layer of the DDPM model) or the timestep dimension (one for a set of timesteps in the denoising process), leading to suboptimal attribute disentanglement.
Iterative Multi-granular Image Editing using Diffusion Models
We hope our work would attract attention to this newly identified, pragmatic problem setting.
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection
SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection; (ii) it can handle shifts in output label space from the pre-trained source network to the target domain; (iii) it does not require access to source data for adaptation.
