SAM3 LiteText Overview SAM3 LiteText was proposed in SAM3 LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision Language Segmentation by Chengxi Zeng, Yuxuan Jiang, Ge Gao, Shuai Wang, Duolikun Danier, Bin Zhu, Stevan Rudinac, David Bull, and Fan Zhang. SAM3 LiteText is a lightweight variant of SAM3 that replaces the heavy SAM3 text encoder (353M parameters) with a compact MobileCLIP based text encoder optimized through knowledge distillation. The SAM3 ViT H image encoder is kept intact. This reduces text encoder parameters by up to 88% while maintaining segmentation performance comparable to the original model. The abstract from the paper is the following: Vision language segmentation models such as SAM3 enable flexible, prompt driven visual grounding, but inherit large, general purpose text encoders originally designed for open ended language understanding. In practice, segmentation prompts are short, structured, and semantically constrained, leading to substantial over provisioning in text encoder capacity and persistent computational and memory overhead. In this paper, we perform a large scale anatomical analysis of text prompting in vision language segmen…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy