Fast SAM2 with Text-Driven Token Pruning

This article likely discusses an improvement to the Segment Anything Model (SAM), focusing on speed and efficiency. The use of 'Text-Driven Token Pruning' suggests a method to optimize the model's processing by selectively removing less relevant tokens based on textual input. This could lead to faster inference times and potentially reduced computational costs. The source being ArXiv indicates this is a research paper, likely detailing the technical aspects of the proposed improvements.

• •Focuses on improving the speed and efficiency of the Segment Anything Model (SAM).
• •Employs 'Text-Driven Token Pruning' to optimize processing.
• •Likely involves reducing computational costs and improving inference times.
• •Presented as a research paper on ArXiv.