Update README.md

README.md

@@ -149,10 +149,11 @@ features are aligned within the same semantic space. This “image-as-text” al
 applications, including text segmentation, retrieval, and visual question answering.
 
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 ### Evaluation on Vision Capability
 
 We present a comprehensive evaluation of the vision encoder’s performance across various domains and tasks. 
@@ -168,16 +169,15 @@ Please refer to our technical report for more details.
 #### text retrial
 
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 #### image segmentation
 
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 #### visual question answering
 
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@@ -200,9 +200,10 @@ Following the previous training paradigm, TokenVL also includes two stages:
 **Stage 1: LLM-guided Token Alignment Training for text parsing tasks.**
 
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 The framework of LLM-guided Token Alignment Training. Existing MLLMs primarily enhance spatial-wise text perception capabilities by integrating localization prompts to predict coordinates. However, this implicit
 method makes it difficult for these models to have a precise understanding. 
 In contrast, the proposed token alignment uses BPE token masks to directly and explicitly align text with corresponding pixels in the input image, enhancing the MLLM’s localization awareness.