Franca：面向可扩展视觉表征学习的嵌套 Matryoshka 聚类
Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
嵌套 Matryoshka 多头部聚类投影器可在不增大模型规模的前提下把特征逐级细化为更细粒度的簇，配合从稠密表征中显式剥离位置偏置的策略，可直接用于改进 DINO 类自监督预训练的稠密特征质量，对分割、深度估计等下游密集预测任务有直接迁移价值。
原文依据 a multi-head clustering projector based on nested Matryoshka representations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 Franca（读作 Fran-ka）：自由之一；这是首个完全开源（数据、代码、权重）的视觉基础模型，其性能与最优的专有模型（如 DINOv2、CLIP、SigLIPv2 等）相当，并在许多情况下超越它们。我们的方法建立在受 Web-SSL 启发的透明训练流程之上，并使用公开可用的数据：ImageNet-21K 以及 ReLAION-2B 的一个子集。除发布模型之外，我们还解决了自监督学习聚类方法中的关键局限。现有方法通过 Sinkhorn-Knopp 等聚类算法将图像特征分配到大型码本中，但往往忽视了簇语义中固有的歧义性。为此，我们引入了一种基于嵌套 Matryoshka 表征的多头部聚类投影器。该设计在不增加模型规模的情况下，将特征逐步细化为越来越细粒度的簇，从而产生更高质量的稠密表征。此外，我们提出了一种新的位置解耦策略，显式地从稠密表征中去除位置偏置。这在多个下游基准上带来了一致的增益，证明了更干净的特征空间的效用。我们的贡献为透明、高性能的视觉模型确立了新标准，并为更广泛的人工智能社区开辟了一条通往更具可复现性与泛化性的基础模型的道路。
英文原文摘要 1419 字符
We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in self-supervised learning clustering methods. Existing approaches assign image features to large codebooks via clustering algorithms such as Sinkhorn-Knopp, but they often overlook the inherent ambiguity in cluster semantics. To address this, we introduce a multi-head clustering projector based on nested Matryoshka representations. This design progressively refines features into increasingly fine-grained clusters without increasing the model size, producing higher-quality dense representations. Additionally, we propose a novel positional disentanglement strategy that explicitly removes positional biases from dense representations.This leads to consistent gains on several downstream benchmarks, demonstrating the utility of cleaner feature spaces. Our contributions establish a new standard for transparent, high-performance vision models and open a path toward more reproducible and generalizable foundation models for the broader AI community.