Combining CLIP and the Segment Anything Model (SAM) is a groundbreaking Vision Foundation Models (VFMs) approach. SAM performs superior segmentation tasks across diverse domains, while CLIP is renowned for its exceptional zero-shot recognition capabilities.  While SAM and CLIP offer significant advantages, they also come with inherent limitations in their original designs. SAM, for instance, cannot…

When the camera and the subject move about one another during the exposure, the result is a typical artifact known as motion blur. Computer vision tasks like autonomous driving, object segmentation, and scene analysis can negatively impact this effect, which blurs or stretches the image’s object contours, diminishing their clarity and detail. To create efficient…

Text-to-image synthesis is a revolutionary technology that converts textual descriptions into vivid visual content. This technology’s significance lies in its potential applications, ranging from artistic digital creation to practical design assistance across various sectors. However, a pressing challenge in this domain is creating models that balance high-quality image generation with computational efficiency, particularly for users…

The rapidly evolving domain of text-to-3D generative methods, the challenge of creating reliable and comprehensive evaluation metrics is paramount. Previous approaches have relied on specific criteria, such as how well a generated 3D object aligns with its textual description. However, these methods often must improve versatility and alignment with human judgment. The need for a…