发表机构
PolyU/RCA; Royal College of Art; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University（香港理工大学/皇家艺术学院; 皇家艺术学院; 香港理工大学工业及系统工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种多智能体系统，用CNN级联处理多数样本，VLM仅作仲裁，以织物分类法为约束，在14类基准上提升准确率并降低90%成本，实现快速CPU推理。
AI 中文摘要
识别织物结构是将纺织品特有的材料信息转化为结构化数字形式以供下游供应链系统使用的前提。纯CNN分类器成本高效，但在视觉上模糊的类别上表现不佳；视觉-语言模型（VLM）泛化能力更强，但每张图像的成本高得多，且在专业领域上不稳定。我们提出了一种多智能体系统，其中CNN级联处理容易的多数样本，VLM仅作为选择性仲裁者被调用，并限制在基于分类法的前三选择。织物分类法作为整个识别过程的约束，以提高准确性并减少VLM调用。同时，CNN级联被蒸馏到较小的参数规模，以减少推理时间并满足实际部署需求。在一个新整理的14类基准上，平坦的ConvNeXt-Tiny基线达到90.45%的top-1准确率和四个最难类别上的76.9%准确率；我们的方法的分层级联达到93.94%的top-1准确率和94.50%的困难类别准确率（提升17.6个百分点）。将VLM触发阈值从60%收紧到<10%，API成本降低约90%，且没有可测量的精度损失。无VLM调用时CPU推理时间≤93毫秒（蒸馏后为9.3毫秒）。每次预测都附带机器可读的推理记录，作为未来供应链文档的切入点。
英文摘要
Recognizing a fabric's structure is a prerequisite for translating textile-specific material information into structured digital form for downstream supply-chain systems. Pure CNN classifiers are cost-efficient but fail on visually ambiguous categories; vision--language models (VLMs) generalize more broadly but cost much more per image and are unstable on specialist domains. We present a multi-agent system in which a CNN cascade handles the easy majority and a VLM is invoked only as a selective arbiter, constrained to a top-3 taxonomy-consistent choice. The fabric taxonomy performs as a constraint for the whole recognition process to increase the accuracy and reduce the VLM calls. Meanwhile, the CNN cascade is distilled to a small parameter size to reduce the inference time and meet the needs of practical deployment. On a newly curated 14-class benchmark, a flat ConvNeXt-Tiny baseline reaches $90.45\,\%$ top-1 and $76.9\,\%$ on the four hardest classes; \method's hierarchical cascade reaches $93.94\,\%$ top-1 and $94.50\,\%$ hard ($+17.6$\,pp). Tightening the VLM trigger from $60\,\%$ to $<\!10\,\%$ cuts API cost by ${\sim}90\,\%$ with no measurable accuracy loss. CPU inference is $\le\!93$\,ms without a VLM call ($9.3$\,ms distilled). Each prediction carries a machine-readable reasoning record, offered as an entry point for future supply-chain documentation.
