Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory
This work addresses computational efficiency and rare-class performance in HOI detection for computer vision applications, representing an incremental improvement.
The paper tackles the problem of Human-Object Interaction (HOI) detection, which suffers from performance drops on rare classes and high computational costs due to long-tailed data distributions, by proposing an efficient adaptive detector that leverages pre-trained vision-language models and achieves competitive results on HICO-DET and V-COCO datasets with reduced training time.
Human Object Interaction (HOI) detection aims to localize and infer the relationships between a human and an object. Arguably, training supervised models for this task from scratch presents challenges due to the performance drop over rare classes and the high computational cost and time required to handle long-tailed distributions of HOIs in complex HOI scenes in realistic settings. This observation motivates us to design an HOI detector that can be trained even with long-tailed labeled data and can leverage existing knowledge from pre-trained models. Inspired by the powerful generalization ability of the large Vision-Language Models (VLM) on classification and retrieval tasks, we propose an efficient Adaptive HOI Detector with Concept-guided Memory (ADA-CM). ADA-CM has two operating modes. The first mode makes it tunable without learning new parameters in a training-free paradigm. Its second mode incorporates an instance-aware adapter mechanism that can further efficiently boost performance if updating a lightweight set of parameters can be afforded. Our proposed method achieves competitive results with state-of-the-art on the HICO-DET and V-COCO datasets with much less training time. Code can be found at https://github.com/ltttpku/ADA-CM.