  We present SLIP (SAM+CLIP), an enhanced architecture for zero-shot object
segmentation. SLIP combines the Segment Anything Model (SAM)
\cite{kirillov2023segment} with the Contrastive Language-Image Pretraining
(CLIP) \cite{radford2021learning}. By incorporating text prompts into SAM using
CLIP, SLIP enables object segmentation without prior training on specific
classes or categories. We fine-tune CLIP on a Pokemon dataset, allowing it to
learn meaningful image-text representations. SLIP demonstrates the ability to
recognize and segment objects in images based on contextual information from
text prompts, expanding the capabilities of SAM for versatile object
segmentation. Our experiments demonstrate the effectiveness of the SLIP
architecture in segmenting objects in images based on textual cues. The
integration of CLIP's text-image understanding capabilities into SAM expands
the capabilities of the original architecture and enables more versatile and
context-aware object segmentation.
