Accelerating Vision Foundation Models with Drop-in Depthwise Convolution
This work addresses inference bottlenecks of large vision models for deployment on resource-constrained devices.
The authors accelerate pretrained Vision Transformers by replacing certain attention heads with depthwise convolutions, achieving 17-20% inference speedup with minimal performance degradation on classification and segmentation tasks.
Pretrained vision foundation models deliver strong performance across tasks with limited fine-tuning. However, their Vision Transformer (ViT) backbones impose high inference costs, limiting deployment on resource-constrained devices. In this work, we accelerate large-scale pretrained ViTs while preserving their feature extraction capabilities by exploiting the intrinsic convolution-like behavior of some attention heads. Specifically, we introduce an efficient depthwise convolution-based layer that serves as a drop-in replacement for these heads. Additionally, we propose simple strategies to identify which heads can be replaced and introduce a fine-tuning procedure that recovers downstream task performance. Across both image classification and segmentation tasks, our method achieves 17-20\% percent inference speedup with minimal performance degradation. We validate the approach through detailed derivations, extensive experiments, and efficiency benchmarks. The reference implementation is publicly available.