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

Understand

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.