Transferable Interactiveness Knowledge for Human-Object Interaction Detection
arXiv:2101.10292 · doi:10.1109/TPAMI.2021.3054048
Abstract
Human-Object Interaction (HOI) detection is an important problem to understand how humans interact with objects. In this paper, we explore interactiveness knowledge which indicates whether a human and an object interact with each other or not. We found that interactiveness knowledge can be learned across HOI datasets and bridge the gap between diverse HOI category settings. Our core idea is to exploit an interactiveness network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression (NIS) before HOI classification in inference. On account of the generalization ability of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. We utilize the human instance and body part features together to learn the interactiveness in hierarchical paradigm, i.e., instance-level and body part-level interactivenesses. Thereafter, a consistency task is proposed to guide the learning and extract deeper interactive visual clues. We extensively evaluate the proposed method on HICO-DET, V-COCO, and a newly constructed PaStaNet-HOI dataset. With the learned interactiveness, our method outperforms state-of-the-art HOI detection methods, verifying its efficacy and flexibility. Code is available at https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network.
TPAMI version of our CVPR2019 paper with a new benchmark PaStaNet-HOI. Code: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network. arXiv admin note: substantial text overlap with arXiv:1811.08264
References in corpus (7)
- Semi-Supervised Classification with Graph Convolutional Networks
- Visual Semantic Role Labeling
- CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
- Care about you: towards large-scale human-centric visual relationship detection
- Detailed 2D-3D Joint Representation for Human-Object Interaction
- PaStaNet: Toward Human Activity Knowledge Engine
- Beyond Holistic Object Recognition: Enriching Image Understanding with Part States