Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts

• URL: http://arxiv.org/abs/2008.01178v5
• Date: Fri, 12 Nov 2021 10:23:11 GMT
• Title: Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
• Authors: Nicolas Gonthier and Sa\"id Ladjal and Yann Gousseau
• Abstract summary: Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. We show that a simple multiple instance approach applied on pre-trained deep features yields excellent performances on non-photographic datasets.
• Score: 1.9336815376402716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed with a domain-specific solution focused on natural images, we show that a simple multiple instance approach applied on pre-trained deep features yields excellent performances on non-photographic datasets, possibly including new classes. The approach does not include any fine-tuning or cross-domain learning and is therefore efficient and possibly applicable to arbitrary datasets and classes. We investigate several flavors of the proposed approach, some including multi-layers perceptron and polyhedral classifiers. Despite its simplicity, our method shows competitive results on a range of publicly available datasets, including paintings (People-Art, IconArt), watercolors, cliparts and comics and allows to quickly learn unseen visual categories.

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