Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection

TL;DR

This paper introduces Prova, a multi-modal prototype classifier that significantly improves vast-vocabulary object detection across various models and settings by leveraging comprehensive multi-modal prototypes.

Contribution

Prova is a simple yet effective multi-modal prototype classifier that enhances recognition performance in vast-vocabulary object detection, addressing the limitations of previous classifiers.

Findings

Prova improves Faster R-CNN, FCOS, and DINO AP by 3.3, 6.2, and 2.9 respectively.

Prova achieves 32.8 base AP and 11.0 novel AP in open-vocabulary detection.

Prova outperforms previous methods with 2.6 and 4.3 gains in base and novel AP.

Abstract

Enabling models to recognize vast open-world categories has been a longstanding pursuit in object detection. By leveraging the generalization capabilities of vision-language models, current open-world detectors can recognize a broader range of vocabularies, despite being trained on limited categories. However, when the scale of the category vocabularies during training expands to a real-world level, previous classifiers aligned with coarse class names significantly reduce the recognition performance of these detectors. In this paper, we introduce Prova, a multi-modal prototype classifier for vast-vocabulary object detection. Prova extracts comprehensive multi-modal prototypes as initialization of alignment classifiers to tackle the vast-vocabulary object recognition failure problem. On V3Det, this simple method greatly enhances the performance among one-stage, two-stage, and DETR-based detectors with only additional projection layers in both supervised and open-vocabulary settings. In particular, Prova improves Faster R-CNN, FCOS, and DINO by 3.3, 6.2, and 2.9 AP respectively in the supervised setting of V3Det. For the open-vocabulary setting, Prova achieves a new state-of-the-art performance with 32.8 base AP and 11.0 novel AP, which is of 2.6 and 4.3 gain over the previous methods.