SAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language Segmentation

This paper conducts an in-depth anatomical study of the SAM3 text encoder, a critical component for vision-language segmentation models, focusing on identifying architectural bottlenecks and proposing optimizations for efficiency. We analyze its contribution to multimodal feature fusion and explore methods to achieve lightweight yet effective segmentation. Our proposed SAM3-LiteText demonstrates significant improvements in computational efficiency without substantial loss in segmentation accuracy, making it suitable for deployment in resource-constrained environments and real-time applications requiring robust vision-language understanding.