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arXiv · 2402.10093

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations

Benedikt Alkin·Lukas Miklautz·Sepp Hochreiter·Johannes Brandstetter
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Abstract

We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. MIM-Refiner is motivated by the insight that strong representations within MIM models generally reside in intermediate layers. Accordingly, MIM-Refiner leverages multiple contrastive heads that are connected to different intermediate layers. In each head, a modified nearest neighbor objective constructs semantic clusters that capture semantic information which improves performance on downstream tasks, including off-the-shelf and fine-tuning settings. The refinement process is short and simple - yet highly effective. Within a few epochs, we refine the features of MIM models from subpar to state-of-the-art, off-the-shelf features. Refining a ViT-H, pre-trained with data2vec 2.0 on ImageNet-1K, sets a new state-of-the-art in linear probing (84.7%) and low-shot classification among models that are pre-trained on ImageNet-1K. MIM-Refiner efficiently combines the advantages of MIM and ID objectives and compares favorably against previous state-of-the-art SSL models on a variety of benchmarks such as low-shot classification, long-tailed classification, clustering and semantic segmentation.

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Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter. 2025-02-20. MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations. https://arxiv.org/abs/2402.10093

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