MIMIC: Masked Image Modeling with Image Correspondences
Keywords: Data curation techniques, Masked Image Modeling, Dense vision tasks, Large scale pretraining, Self supervised learning, Datasets
TL;DR: We propose a pretraining data curation method to mine multi-view image pairs from real videos and synthetic 3D environments for downstream dense vision tasks.
Abstract: Dense pixel-specific representation learning at scale has been bottlenecked due to the unavailability of large-scale multi-view datasets. Current methods for building effective pretraining datasets heavily rely on annotated 3D meshes, point clouds, and camera parameters from simulated environments, preventing them from building datasets from real-world data sources where such metadata is lacking. We propose a pretraining dataset-curation approach that does not require any additional annotations. Our method allows us to generate multi-view datasets from both real-world videos and simulated environments at scale. Specifically, we experiment with two scales: MIMIC-1M with 1.3M and MIMIC-3M with 3.1M multi-view image pairs. We train multiple models with different masked image modeling objectives to showcase the following findings: Representations trained on our automatically generated MIMIC-3M outperform those learned from expensive crowdsourced datasets (ImageNet-1K) and those learned from synthetic environments (MULTIVIEW-HABITAT) on two dense geometric tasks: depth estimation on NYUv2 (↑1.7%), and surface normals estimation on Taskonomy (↓2.05%). For dense tasks which also require object understanding, we outperform MULTIVIEW- HABITAT, on semantic segmentation on ADE20K (↑3.89%), pose estimation on MSCOCO (↑9.4%), and reduce the gap with models pre-trained on the object-centric expensive ImageNet-1K. We outperform even when the representations are frozen, and when downstream training data is limited to few-shot. Larger dataset (MIMIC-3M) significantly improves performance, which is promising since our curation method can arbitrarily scale to produce even larger datasets.
Primary Area: datasets and benchmarks
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Submission Number: 8576
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