论文标题
级联的人类对象互动识别
Cascaded Human-Object Interaction Recognition
论文作者
论文摘要
人类对象相互作用（HOI）识别已得到快速进步，但大多数现有模型仅限于单阶段推理管道。考虑到任务的内在复杂性，我们引入了级联体系结构，以进行多阶段，粗到1的HOI理解。在每个阶段，实例本地化网络逐渐完善了HOI建议，并将其馈入交互识别网络。在上一个阶段，两个网络中的每个网络也都连接到其前身，从而实现了跨阶段信息的传播。交互识别网络有两个关键部分：高质量HOI建议选择的关系排名模块和一个三流分类器用于关系预测。凭借我们精心设计的以人为中心的关系特征，这两个模块在有效的互动理解上共同致力于。在边界盒级别上的关系中，我们还可以使我们的框架灵活地执行精细的像素关系分割；这为更好的关系建模提供了新的瞥见。我们的方法在关系检测和细分任务上，在上下文挑战中达到了ICCV2019人的$ 1^{st} $。它还显示了V-Coco的有希望的结果。
Rapid progress has been witnessed for human-object interaction (HOI) recognition, but most existing models are confined to single-stage reasoning pipelines. Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. Each of the two networks is also connected to its predecessor at the previous stage, enabling cross-stage information propagation. The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding. Further beyond relation detection on a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. Our approach reached the $1^{st}$ place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation tasks. It also shows promising results on V-COCO.
