add figures

README.md

@@ -2,15 +2,22 @@
 This paper presents a model **SEEM** that can Segment Everything Everywhere all at once. Our SEEM allows users to easily segment visual an image using prompts of different types including visual prompts (points, marks, boxes, scribbles and image segments) and language prompts (text and audio), etc. It can also handle any combination of prompts or generalize to custom prompts. 
 ## :bulb: Highlight Extension Projects
 We emphasize $4$ important features of **SEEM** here.
-1. Versatility: work on various types of prompts;
-2. Compositionaliy: deal with any compositions of prompts;
-3. Interactive: dealmulti-round interactions with human because **SEE** has a memory prompt to store the session history;
-4. Semantic awareness: give a semantic label to any predicted mask;
+1. **Versatility**: work on various types of prompts;
+2. **Compositionaliy**: deal with any compositions of prompts;
+3. **Interactive**: dealmulti-round interactions with human because **SEE** has a memory prompt to store the session history;
+4. **Semantic awareness**: give a semantic label to any predicted mask;
 
+
+![SEEM design](assets/intro.png?raw=true)
+A breif introduction of all the generic and interactive segmentation tasks we can do. Try our demo at xxx.
 ## 🔥Click, scribble to mask
-![SEEM design](assets/click2mask.png?raw=true)
+With a simple click or stoke from the user, we can generate the masks and the corresponding category labels for it.
+![SEEM design](assets/click.png?raw=true)
 ## 🔥Text to mask
-## 🔥Referring image segmentation to mask
+## 🔥Referring image to mask
+With a simple click or stroke on the referring image, the model is able to segment the objects with similar semantics on the target images.
+![SEEM design](assets/refer-elefant.png?raw=true)
+![SEEM design](assets/refer_river.png?raw=true)
 ## 🔥Combination of different prompts to mask
 
 ## :robot: Run click to mask demo