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Detecting Human-Object Interaction with Mixed Supervision

Abstract : Human object interaction (HOI) detection is an important task in image understanding and reasoning. It is in a form of HOI triplet human, verb, object , requiring bounding boxes for human and object, and action between them for the task completion. In other words, this task requires strong supervision for training that is however hard to procure. A natural solution to overcome this is to pursue weakly-supervised learning, where we only know the presence of certain HOI triplets in images but their exact location is unknown. Most weakly-supervised learning methods do not make provision for leveraging data with strong supervision, when they are available; and indeed a naive combination of this two paradigms in HOI detection fails to make contributions to each other. In this regard we propose a mixed-supervised HOI detection pipeline: thanks to a specific design of momentum-independent learning that learns seamlessly across these two types of supervision. Moreover, in light of the annotation insufficiency in mixed supervision, we introduce an HOI element swapping technique to synthesize diverse and hard negatives across images and improve the robustness of the model. Our method is evaluated on the challenging HICO-DET dataset. It performs close to or even better than many fully-supervised methods by using a mixed amount of strong and weak annotations; furthermore, it outperforms representative state of the art weaklyand fully-supervised methods under the same supervision.
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Contributor : Ewa Kijak Connect in order to contact the contributor
Submitted on : Friday, December 4, 2020 - 4:28:56 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Friday, March 5, 2021 - 7:23:52 PM


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  • HAL Id : hal-03041004, version 1


Suresh Kirthi Kumaraswamy, Miaojing Shi, Ewa Kijak. Detecting Human-Object Interaction with Mixed Supervision. WACV 2021 - Winter Conference on Applications of Computer Vision, Jan 2021, Waikoloa / Virtual, United States. pp.1-10. ⟨hal-03041004⟩



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