Title: Compact GSPN: Scaling Spatial Propagation to Vision Foundation Models
Primary area: foundation or frontier models, including LLMs
Keywords: Efficient vision foundation model

Abstract: Scaling vision foundation models is limited by the quadratic cost of self-attention. Generalized Spatial Propagation Networks (GSPN) provide a linear-time alternative that propagates context directly on the 2D grid and removes positional embeddings, but have not been scaled to foundation-level training. We present Compact GSPN (C-GSPN), a ViT block with a compressed propagation space that preserves accuracy while cutting propagation latency by nearly 10×, complemented by lightweight projections and fused CUDA kernels for further efficiency. To pretrain at scale, we use a two-stage distillation scheme with module-wise supervision and end-to-end alignment. In a representative 1K configuration (batch32, C=1152), C-GSPN yields up to 2× speedup, while maintaining competitive zero-shot accuracy and improving segmentation by +2.1%. Extensive experiments and ablations confirm that the proposed compression and two-stage distillation are key to achieving strong transfer while substantially reducing compute, offering a practical path toward subquadratic vision foundation models.