Summary of Zero Shot Context-based Object Segmentation Using Slip (sam+clip), by Saaketh Koundinya Gundavarapu et al.
Zero Shot Context-Based Object Segmentation using SLIP (SAM+CLIP)
by Saaketh Koundinya Gundavarapu, Arushi Arora, Shreya Agarwal
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary SLIP, an enhanced architecture for zero-shot object segmentation, combines the Segment Anything Model (SAM) with Contrastive Language-Image Pretraining (CLIP). By incorporating text prompts into SAM using CLIP, SLIP enables object segmentation without prior training on specific classes or categories. The model is fine-tuned on a Pokemon dataset 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. Experiments show the effectiveness of the SLIP architecture in segmenting objects in images based on textual cues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SLIP is a new way to separate objects in pictures using words. It’s like having a superpower that lets you find specific things in a picture just by asking a question about it. Right now, computers are not very good at doing this without some training first. But SLIP changes that. It takes two powerful tools, SAM and CLIP, and combines them to make something totally new. This helps the computer understand what’s going on in a picture based on what we ask about it. |
Keywords
» Artificial intelligence » Pretraining » Sam » Zero shot
