I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation
This work addresses efficiency challenges for deploying semantic segmentation models in real-world, resource-limited applications, representing an incremental improvement through quantization and architectural modifications.
The paper tackles the problem of deploying Vision Transformers for semantic segmentation on resource-constrained devices by introducing I-Segmenter, a fully integer-only framework that reduces model size by up to 3.8x and speeds up inference by up to 1.2x while maintaining accuracy within 5.1% of its FP32 baseline.
Vision Transformers (ViTs) have recently achieved strong results in semantic segmentation, yet their deployment on resource-constrained devices remains limited due to their high memory footprint and computational cost. Quantization offers an effective strategy to improve efficiency, but ViT-based segmentation models are notoriously fragile under low precision, as quantization errors accumulate across deep encoder-decoder pipelines. We introduce I-Segmenter, the first fully integer-only ViT segmentation framework. Building on the Segmenter architecture, I-Segmenter systematically replaces floating-point operations with integer-only counterparts. To further stabilize both training and inference, we propose $λ$-ShiftGELU, a novel activation function that mitigates the limitations of uniform quantization in handling long-tailed activation distributions. In addition, we remove the L2 normalization layer and replace bilinear interpolation in the decoder with nearest neighbor upsampling, ensuring integer-only execution throughout the computational graph. Extensive experiments show that I-Segmenter achieves accuracy within a reasonable margin of its FP32 baseline (5.1 % on average), while reducing model size by up to 3.8x and enabling up to 1.2x faster inference with optimized runtimes. Notably, even in one-shot PTQ with a single calibration image, I-Segmenter delivers competitive accuracy, underscoring its practicality for real-world deployment.