FreeA: Human-object Interaction Detection using Free Annotation Labels

• URL: http://arxiv.org/abs/2403.01840v1
• Date: Mon, 4 Mar 2024 08:38:15 GMT
• Title: FreeA: Human-object Interaction Detection using Free Annotation Labels
• Authors: Yuxiao Wang, Zhenao Wei, Xinyu Jiang, Yu Lei, Weiying Xue, Jinxiu Liu, Qi Liu
• Abstract summary: We propose a novel self-adaption language-driven HOI detection method, termed as FreeA, without labeling. FreeA matches image features of human-object pairs with HOI text templates, and a priori knowledge-based mask method is developed to suppress improbable interactions. Experiments on two benchmark datasets show that FreeA state-of-the-art performance among weakly supervised HOI models.
• Score: 9.537338958326181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent human-object interaction (HOI) detection approaches rely on high cost of manpower and require comprehensive annotated image datasets. In this paper, we propose a novel self-adaption language-driven HOI detection method, termed as FreeA, without labeling by leveraging the adaptability of CLIP to generate latent HOI labels. To be specific, FreeA matches image features of human-object pairs with HOI text templates, and a priori knowledge-based mask method is developed to suppress improbable interactions. In addition, FreeA utilizes the proposed interaction correlation matching method to enhance the likelihood of actions related to a specified action, further refine the generated HOI labels. Experiments on two benchmark datasets show that FreeA achieves state-of-the-art performance among weakly supervised HOI models. Our approach is +8.58 mean Average Precision (mAP) on HICO-DET and +1.23 mAP on V-COCO more accurate in localizing and classifying the interactive actions than the newest weakly model, and +1.68 mAP and +7.28 mAP than the latest weakly+ model, respectively. Code will be available at https://drliuqi.github.io/.

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