UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

• URL: http://arxiv.org/abs/2312.12664v1
• Date: Tue, 19 Dec 2023 23:34:43 GMT
• Title: UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection
• Authors: Bumsoo Kim, Taeho Choi, Jaewoo Kang, Hyunwoo J. Kim
• Abstract summary: We propose a one-stage meta-architecture for HOI detection powered by a novel union-level detector. Our one-stage detector for human-object interaction shows a significant reduction in interaction prediction time 4x14x.
• Score: 35.2385914946471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Most prior works have obtained this information with a bottom-up approach, where the objects are first detected and the interactions are predicted sequentially by pairing the objects. This is a major bottleneck in HOI detection inference time. To tackle this problem, we propose UnionDet, a one-stage meta-architecture for HOI detection powered by a novel union-level detector that eliminates this additional inference stage by directly capturing the region of interaction. Our one-stage detector for human-object interaction shows a significant reduction in interaction prediction time 4x~14x while outperforming state-of-the-art methods on two public datasets: V-COCO and HICO-DET.

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