Is Object Detection Necessary for Human-Object Interaction Recognition?
It addresses the problem of simplifying HOI recognition for computer vision researchers by eliminating reliance on detection supervision, showing a significant performance gain.
This paper tackles human-object interaction recognition without object detection or pose supervision, achieving 60.5 mAP on the HICO dataset, which outperforms detection-supervised state-of-the-art methods by 13.4 mAP.
This paper revisits human-object interaction (HOI) recognition at image level without using supervisions of object location and human pose. We name it detection-free HOI recognition, in contrast to the existing detection-supervised approaches which rely on object and keypoint detections to achieve state of the art. With our method, not only the detection supervision is evitable, but superior performance can be achieved by properly using image-text pre-training (such as CLIP) and the proposed Log-Sum-Exp Sign (LSE-Sign) loss function. Specifically, using text embeddings of class labels to initialize the linear classifier is essential for leveraging the CLIP pre-trained image encoder. In addition, LSE-Sign loss facilitates learning from multiple labels on an imbalanced dataset by normalizing gradients over all classes in a softmax format. Surprisingly, our detection-free solution achieves 60.5 mAP on the HICO dataset, outperforming the detection-supervised state of the art by 13.4 mAP