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학술지 Three-stream Network with Context Convolution Module for Human-object Interaction Detection
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저자
시아다리, 한미경, 윤현진
발행일
202004
출처
ETRI Journal, v.42 no.2, pp.230-238
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2019-0230
협약과제
19MH1500, 5G 기반의 스마트시티 서비스 개발 및 실증, 한미경
초록
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.
키워드
context convolution module, deep learning, HOI detection, human?뱋bject interactions, three-stream network
KSP 제안 키워드
Art performance, Benchmark datasets, Computer Vision(CV), Context Information, Detection Method, Feature Map, Human-Object Interaction, Interaction detection, Separable Filters, Stream network, deep learning(DL)