Causal Intervention for Object Detection

Weiqing Huang, Miao Jiang, Min Li, Bo Meng, Junxing Ren, Shixian Zhao, Ruwen Bai, Yang Yang

Abstract: We present a causal intervention module (CIM) to improve object detection methods. State-of-the-art object detectors learn the association between image pixels and bounding boxes with labels, which implicitly use contextual information in the backbone. Intuitively, context is such a rich source of information that an improvement due to contextual information is relatively modest. Inspired by this, we use the context explicitly in a novel framework of causal intervention for object detection. Specifically, we use a structural causal model to reveal how context confounders affect the object detection model, and adopt causal intervention to deal with the effect. The proposed CIM is applied to a two-stage object detection baseline, and extensive experiments show its effectiveness.

External IDs:dblp:conf/ictai/HuangJLMRZB021