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Journal Article
Three‐stream network with context convolution module for human–object interaction detection
- Authors
- Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon
- Issue Date
- 2020-04
- Citation
- ETRI Journal, v.42, no.2, pp.230-238
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- Human?뱋bject interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1혻×혻1 or 3혻×혻3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.
- KSP Keywords
- Art performance, Benchmark datasets, Computer Vision(CV), Context Information, Detection Method, Feature Map, Interaction detection, Object interaction, Separable Filters, Stream network, feature extractor