3 accepted papers
Diffusion models generate high-quality images through progressive denoising but are computationally intensive due to large model sizes and repeated sampling. Knowledge distillation--transferring knowledge from a complex teacher to a simpler student model--has been widely studied in recognition tasks…
Diffusion models have emerged as a cornerstone of generative modeling, capable of producing high-quality images through a progressive denoising process. However, their remarkable performance comes with substantial computational costs, driven by large model sizes and the need for multiple sampling st…
We aim to solve the problem of spatially localizing composite instructions referring to space: space grounding. Compared to current instance grounding, space grounding is challenging due to the ill-posedness of identifying locations referred to by discrete expressions and the compositional ambiguity…