This paper introduces an innovative approach for image matting that redefines the traditional
regression-based task as a generative modeling challenge. Our method harnesses the …

Recent advancements in text-to-image generative models have demonstrated a remarkable
ability to capture a deep semantic understanding of images. In this work, we leverage this …

The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised
image segmentation. To apply SAM to surgical instrument segmentation, a common …

Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …

Images produced by text-to-image diffusion models might not always faithfully represent the
semantic intent of the provided text prompt where the model might overlook or entirely fail to …

Recent strides in the development of diffusion models exemplified by advancements such as
Stable Diffusion have underscored their remarkable prowess in generating visually …

Recent text-to-image (T2I) diffusion models have achieved remarkable progress in
generating high-quality images given text-prompts as input. However, these models fail to …

We introduce the Multi-Instance Generation (MIG) task, which focuses on generating
multiple instances within a single image, each accurately placed at predefined positions with …

Quantum computing has recently emerged as a transformative technology. Yet, its promised
advantages rely on efficiently translating quantum operations into viable physical …

Existing natural image matting algorithms inevitably have flaws in their predictions on
difficult cases, and their one-step prediction manner cannot further correct these errors. In …