no code implementations • 30 Mar 2024 • Ayan Banerjee, Nityanand Mathur, Josep Lladós, Umapada Pal, Anjan Dutta
In response, this work introduces SVGCraft, a novel end-to-end framework for the creation of vector graphics depicting entire scenes from textual descriptions.
no code implementations • 27 Feb 2024 • Shyam Marjit, Harshit Singh, Nityanand Mathur, Sayak Paul, Chia-Mu Yu, Pin-Yu Chen
In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements.
no code implementations • 4 Dec 2023 • Nityanand Mathur, Shyam Marjit, Abhra Chaudhuri, Anjan Dutta
With the goal of understanding the visual concepts that CLIP associates with text prompts, we show that the latent space of CLIP can be visualized solely in terms of linear transformations on simple geometric primitives like circles and straight lines.