The field of computer vision is moving towards improving image synthesis and scene understanding. Researchers are exploring new methods to quantify and mitigate memorization in diffusion models, which can lead to copyright issues. Additionally, there is a growing interest in discovering object dependencies and relationships within scenes, with approaches such as Visual Jenga and BOOTPLACE showing promising results. These methods have the potential to enhance our understanding of complex scenes and improve image synthesis tasks. Furthermore, training-free text-to-image synthesis models are being developed to address issues such as mislocated objects and mismatched attributes. Noteworthy papers include:

• Quantifying the Ease of Reproducing Training Data in Unconditional Diffusion Models, which proposes a method to quantify memorization in diffusion models.
• BOOTPLACE, which introduces a novel paradigm for object placement learning.
• Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis, which proposes a training-free approach for spatially coherent text-to-image synthesis.