arXiv:2509.10334v2 Announce Type: replace-cross Abstract: 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 Segment
The proliferation of AI models, especially ViTs, demands more efficient deployment, and advancements in quantization techniques are making this possible for previously challenging architectures.
Efficient ViT deployment on resource-constrained devices could significantly expand AI's application scope, particularly in edge computing and mobile AI, reducing computational overhead and energy consumption.
The ability to run complex ViT-based semantic segmentation models with integer-only operations changes the feasibility and energy footprint of advanced computer vision applications outside of data centers.
- · Edge AI device manufacturers
- · Embedded systems developers
- · Mobile AI applications
- · Computer vision integrators
- · Companies reliant solely on high-power, cloud-based AI
- · Hardware vendors without strong quantization support
Wider adoption of Vision Transformers for real-time semantic segmentation on on-device platforms due to reduced computational requirements.
Increased demand for specialized AI accelerators optimized for integer arithmetic and efficient memory access.
Democratization of advanced AI capabilities leading to new applications in IoT, robotics, and autonomous systems with lower hardware costs.
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