Learning Human-Object Interaction Detection using Interaction Points (2003.14023v1)

Abstract: Understanding interactions between humans and objects is one of the fundamental problems in visual classification and an essential step towards detailed scene understanding. Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them. Most existing HOI detection approaches are instance-centric where interactions between all possible human-object pairs are predicted based on appearance features and coarse spatial information. We argue that appearance features alone are insufficient to capture complex human-object interactions. In this paper, we therefore propose a novel fully-convolutional approach that directly detects the interactions between human-object pairs. Our network predicts interaction points, which directly localize and classify the inter-action. Paired with the densely predicted interaction vectors, the interactions are associated with human and object detections to obtain final predictions. To the best of our knowledge, we are the first to propose an approach where HOI detection is posed as a keypoint detection and grouping problem. Experiments are performed on two popular benchmarks: V-COCO and HICO-DET. Our approach sets a new state-of-the-art on both datasets. Code is available at https://github.com/vaesl/IP-Net.

Overview of "Learning Human-Object Interaction Detection using Interaction Points"

The paper "Learning Human-Object Interaction Detection using Interaction Points" addresses the complex problem of detecting interactions between humans and objects within images. This task, known as human-object interaction (HOI) detection, involves not only localizing both the human and the corresponding object but also identifying the type of interaction occurring between them. The authors introduce a novel fully-convolutional framework that eschews the traditional instance-centric model for a more streamlined approach based on interaction points.

Key Contributions

The authors propose a method where HOI detection is reframed as a keypoint detection and grouping problem, introducing the concept of 'interaction points'. This approach contrasts with many existing methods that rely heavily on appearance features and multi-stream architectures, which are computationally expensive and may not effectively capture the complex spatial relationships inherent in HOI tasks. Notably, this work is pioneering in its application of anchor-free object detection principles to the domain of human-object interactions.

Key technical components include:

• Interaction Point Prediction: The proposed architecture generates an interaction point that directly localizes and informs the classification of the interaction.
• Interaction Vector Generation: The method predicts dense interaction vectors to associate interactions with specific human and object detections within the scene.
• Interaction Grouping Scheme: A novel scheme is employed to pair detected interaction points and vectors with human and object instances, thus culminating in the final interaction prediction.

Experimental Evaluation

The approach is validated on two benchmark datasets, V-COCO and HICO-DET, demonstrating state-of-the-art performance. This improvement is particularly highlighted in the role mAP scores, where the method surpasses previous benchmarks. The paper reports a significant performance leap over existing methods, indicating the efficacy of reframing the problem in terms of interaction points.

Implications and Future Directions

Practically, this work contributes an efficient alternative to existing HOI detection frameworks, effectively lowering computational costs while maintaining high accuracy. Theoretically, it opens potential research pathways into further exploring anchor-free detection methods in other complex relationship detection tasks.

The approach highlights the potential for keypoint estimation techniques to address complex visual detection tasks, suggesting future exploration in refining these methods to capture even subtler interaction nuances. Additionally, the framework's adaptability to other domains or more complex datasets presents a promising area of investigation. Future work might involve integrating additional contextual or temporal information to further enhance interaction detection accuracy.

Overall, the paper contributes a significant advance in human-object interaction detection, emphasizing both the methodological shift towards interaction-centric detection and a practical framework suitable for real-world applications.