Learning to Detect Human-Object Interactions
This work addresses a fundamental computer vision problem for semantic understanding of interactions, but it is incremental as it builds on existing benchmarks and methods.
The paper tackles the problem of detecting human-object interactions in static images by introducing a new benchmark, HICO-DET, and proposing HO-RCNN with Interaction Patterns to characterize spatial relations, resulting in significant performance improvements over baselines.
We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.