CLIP

The flexibility to accurately interpret complex visual information is a vital focus of multimodal large language models (MLLMs). Recent work shows that enhanced visual perception significantly reduces hallucinations and improves performance on resolution-sensitive tasks,...

A picture can convey an important deal, yet it can also be marred by various issues comparable to motion blur, haze, noise, and low dynamic range. These problems, commonly known as degradations in low-level...

Contrastive models like CLIP have been shown to learn robust representations of images that capture each semantics and magnificence. To leverage these representations for image generation, we propose a two-stage model: a previous that...