iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
Abstrak
Recent years have witnessed rapid progress in detecting and recognizing individual object instances. To understand the situation in a scene, however, computers need to recognize how humans interact with surrounding objects. In this paper, we tackle the challenging task of detecting human-object interactions (HOI). Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction. To exploit these cues, we propose an instance-centric attention module that learns to dynamically highlight regions in an image conditioned on the appearance of each instance. Such an attention-based network allows us to selectively aggregate features relevant for recognizing HOIs. We validate the efficacy of the proposed network on the Verb in COCO and HICO-DET datasets and show that our approach compares favorably with the state-of-the-arts.
Topik & Kata Kunci
Penulis (3)
Chen Gao
Yuliang Zou
Jia-Bin Huang
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2018
- Bahasa
- en
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- Sumber Database
- Semantic Scholar
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- Open Access ✓