Paper Detail
Niccolò Cavagnero, Narges Norouzi, Gijs Dubbelman, Daan de Geus
Vision Foundation Models (VFMs) pre-trained at scale enable a single frozen encoder to serve multiple downstream tasks simultaneously. Recent VFM-based encoder-only models for image and video segmentation, such as EoMT and VidEoMT, achieve competitive accuracy with remarkably low latency, yet they require finetuning the encoder, sacrificing the multi-task encoder sharing that makes VFMs practically attractive for large-scale deployment. To reconcile encoder-only simplicity and speed with frozen VFM features, we propose the Plain Mask Decoder (PMD), a fast Transformer-based segmentation decoder that operates on top of frozen VFM features. The resulting model, the Plain Mask Transformer (PMT), preserves the architectural simplicity and low latency of encoder-only designs while keeping the encoder representation unchanged and shareable. The design seamlessly applies to both image and video segmentation, inheriting the generality of the encoder-only framework. On standard image segmentation benchmarks, PMT matches the frozen-encoder state of the art while running up to ~3x faster. For video segmentation, it even performs on par with fully finetuned methods, while being up to 8x faster than state-of-the-art frozen-encoder models. Code: https://github.com/tue-mps/pmt.
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@misc{cavagnero2026pmt,
title = {PMT: Plain Mask Transformer for Image and Video Segmentation with Frozen Vision Encoders},
author = {Niccolò Cavagnero and Narges Norouzi and Gijs Dubbelman and Daan de Geus},
year = {2026},
abstract = {Vision Foundation Models (VFMs) pre-trained at scale enable a single frozen encoder to serve multiple downstream tasks simultaneously. Recent VFM-based encoder-only models for image and video segmentation, such as EoMT and VidEoMT, achieve competitive accuracy with remarkably low latency, yet they require finetuning the encoder, sacrificing the multi-task encoder sharing that makes VFMs practically attractive for large-scale deployment. To reconcile encoder-only simplicity and speed with frozen },
url = {https://huggingface.co/papers/2603.25398},
keywords = {Vision Foundation Models, encoder-only models, image segmentation, video segmentation, Transformer-based decoder, frozen VFM features, Plain Mask Decoder, Plain Mask Transformer, code available, huggingface daily},
eprint = {2603.25398},
archiveprefix = {arXiv},
}
{}