Retrieval-Augmented Open-Vocabulary Object Detection
Authors: Jooyeon Kim, Eulrang Cho, Sehyung Kim, Hyunwoo J. Kim

Open-vocabulary object detection (OVD) has been studied with Vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector using 'positive' pseudo-labels with additional 'class' names e.g. sock iPod and alligator. To extend the previous methods in two aspects we propose Retrieval-Augmented Losses and visual Features (RALF). Our method retrieves related 'negative' classes and augments loss functions. Also visual features are augmented with 'verbalized concepts' of classes e.g. worn on the feet handheld music player and sharp teeth. Specifically RALF consists of two modules: Retrieval Augmented Losses (RAL) and Retrieval-Augmented visual Features (RAF). RAL constitutes two losses reflecting the semantic similarity with negative vocabularies. In addition RAF augments visual features with the verbalized concepts from a large language model (LLM). Our experiments demonstrate the effectiveness of RALF on COCO and LVIS benchmark datasets. We achieve improvement up to 3.4 box AP_ 50 ^ \text N   on novel categories of the COCO dataset and 3.6 mask AP_ \text r   gains on the LVIS dataset. Code is available at https://github.com/mlvlab/RALF.