EinsPT: Efficient Instance-Aware Pre-Training of Vision Foundation Models

In this study, we introduce EinsPT, an efficient instance-aware pre-training paradigm designed to reduce the transfer gap between vision foundation models and downstream instance-level tasks. Unlike conventional image-level pre-training that relies solely on unlabeled images, EinsPT leverages both image reconstruction and instance annotations to learn representations that are spatially coherent and instance discriminative. To achieve this efficiently, we propose a proxy-foundation architecture that decouples high-resolution and low-resolution learning: the foundation model processes masked low-resolution images for global semantics, while a lightweight proxy model operates on complete high-resolution images to preserve fine-grained details. The two branches are jointly optimized through reconstruction and instance-level prediction losses on fused features. Extensive experiments demonstrate that EinsPT consistently enhances recognition accuracy across various downstream tasks with substantially reduced computational cost, while qualitative results further reveal improved instance perception and completeness in visual representations. Code is available at github.com/feufhd/EinsPT.