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 segmentation, covering 404,796 real prompts across multiple benchmarks. Our analysis reveals severe redundancy: most context windows are underutilized, vocabulary usage is highly sparse, and text embeddings lie on low-dimensional manifold despite high-dimensional representations. Motivated by these findings, we propose SAM3-LiteText, a lightweight text encoding framework that replaces the original SAM3 text encoder with a compact MobileCLIP student that is optimized by knowledge distillation. Extensive experiments on image and video segmentation benchmarks show that SAM3-LiteText reduces text encoder parameters by up to 88%, substantially reducing static memory footprint, while maintaining segmentation performance comparable to the original model. Code: https://github.com/SimonZeng7108/efficientsam3/tree/sam3_litetext.

翻译：诸如SAM3之类的视觉语言分割模型能够实现灵活的、提示驱动的视觉定位，但其继承了原本为开放式语言理解设计的大型通用文本编码器。实际上，分割提示词通常简短、结构化且语义受限，这导致文本编码器能力存在大量过度配置，并带来持续的计算和内存开销。本文对视觉语言分割中的文本提示进行了大规模解剖学分析，涵盖了多个基准测试中的404,796个真实提示。我们的分析揭示了严重的冗余：大多数上下文窗口未得到充分利用，词汇使用高度稀疏，并且尽管采用高维表示，文本嵌入却位于低维流形上。基于这些发现，我们提出了SAM3-LiteText，一个轻量级文本编码框架，它用一个紧凑的MobileCLIP学生模型替代了原始的SAM3文本编码器，并通过知识蒸馏进行优化。在图像和视频分割基准测试上的大量实验表明，SAM3-LiteText将文本编码器参数减少了高达88%，显著降低了静态内存占用，同时保持了与原始模型相当的分割性能。代码：https://github.com/SimonZeng7108/efficientsam3/tree/sam3_litetext。