Diagnosing Human-object Interaction Detectors

Although we have witnessed significant progress in human-object interaction (HOI) detection with increasingly high mAP (mean Average Precision), a single mAP score is too concise to obtain an informative summary of a model's performance and to understand why one approach is better than another. In this paper, we introduce a diagnosis toolbox for analyzing the error sources of the existing HOI detection models. We first conduct holistic investigations in the pipeline of HOI detection, consisting of human-object pair detection and then interaction classification. We define a set of errors and the oracles to fix each of them. By measuring the mAP improvement obtained from fixing an error using its oracle, we can have a detailed analysis of the significance of different errors. We then delve into the human-object detection and interaction classification, respectively, and check the model's behavior. For the first detection task, we investigate both recall and precision, measuring the coverage of ground-truth human-object pairs as well as the noisiness level in the detections. For the second classification task, we compute mAP for interaction classification only, without considering the detection scores. We also measure the performance of the models in differentiating human-object pairs with and without actual interactions using the AP (Average Precision) score. Our toolbox is applicable for different methods across different datasets and available at https://github.com/neu-vi/Diag-HOI.