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  # LISA: Reasoning Segmentation Via Large Language Model
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- This is the official implementation of ***LISA (Large-language Instructed Segmentation Assistant)***. In this work, we propose a new segmentation task --- ***reasoning segmentation***. The task is designed to output a segmentation mask given a complex and implicit query text. We establish a benchmark comprising over one thousand image-instruction pairs, incorporating intricate reasoning and world knowledge for evaluation purposes. Finally, we present LISA: Large-language Instructed Segmentation Assistant, which inherits the language generation capabilities of the multi-modal Large Language Model (LLM) while also possessing the ability to produce segmentation masks.
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- For more details, please refer to:
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- <p align="center"> <img src="imgs/fig_teaser4_crop.png" width="100%"> </p>
 
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  **LISA: Reasoning Segmentation Via Large Language Model [[Paper]()]** <br />
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  [Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ&hl=zh-CN),
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  <p align="center"> <img src="imgs/fig_overview_v6_crop.png" width="100%"> </p>
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  ### Experimental results
 
 
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  <p align="center"> <img src="imgs/Table1.png" width="100%"> </p>
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  # LISA: Reasoning Segmentation Via Large Language Model
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+ This is the official implementation of ***LISA (Large-language Instructed Segmentation Assistant)***.
 
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+ In this work, we propose a new segmentation task --- ***reasoning segmentation***. The task is designed to output a segmentation mask given a complex and implicit query text. We establish a benchmark comprising over one thousand image-instruction pairs, incorporating intricate reasoning and world knowledge for evaluation purposes. Finally, we present LISA: Large-language Instructed Segmentation Assistant, which inherits the language generation capabilities of the multi-modal Large Language Model (LLM) while also possessing the ability to produce segmentation masks.
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+ For more details, please refer to:
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  **LISA: Reasoning Segmentation Via Large Language Model [[Paper]()]** <br />
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  [Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ&hl=zh-CN),
 
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  <p align="center"> <img src="imgs/fig_overview_v6_crop.png" width="100%"> </p>
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  ### Experimental results
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+ <p align="center"> <img src="imgs/fig_teaser4_crop.png" width="100%"> </p>
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+
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  <p align="center"> <img src="imgs/Table1.png" width="100%"> </p>
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