Web-Scale Training Unleashed: Deepmind Introduces OWLv2 and OWL-ST, th …

Open-vocabulary object detection is a critical aspect of various real-world computer vision tasks. However, the limited availability of detection training data and the fragility of pre-trained models often lead to subpar performance and scalability issues.

To tackle this challenge, the DeepMind research team introduces the OWLv2 model in their latest paper, “Scaling Open-Vocabulary Object Detection.” This optimized architecture improves training efficiency and incorporates the OWL-ST self-training recipe, substantially enhancing detection performance and achieving state-of-the-art results in the open-vocabulary detection task.

The primary objective of this work is to optimize the label space, annotation filtering, and training efficiency for the open-vocabulary detection self-training approach, ultimately achieving robust and scalable open-vocabulary performance with limited labeled data.

The proposed self-training approach consists of three key steps:

The team employs an existing open-vocabulary detector to perform open box detection on WebLI, a large-scale dataset of web image-text pairs.

They utilize OWL-ViT CLIP-L/14 to annotate all the WebLI images with bounding box pseudo annotations.

They fine-tune the trained model using human-annotated detection data, further refining its performance.

Notably, the researchers employ a variant of the OWL-ViT architecture to train more effective detectors. This architecture leverages contrastively trained image-text models to initialize image and text encoders while the detection heads are randomly initialized.

During the training stage, the team employs the same losses and augments queries with “pseudo-negatives” from the OWL-ViT architecture, optimizing training efficiency to maximize the utilization of the available labeled images. 

They also incorporate previously proposed practices for large-scale Transformer training to enhance training efficiency further. As a result, the OWLv2 model reduces training FLOPS by approximately 50% and accelerates training throughput by 2× compared to the original OWL-ViT model.

The team compares their proposed approach with previous state-of-the-art open-vocabulary detectors in their empirical study. The OWL-ST technique improves Average Precision (AP) on LVIS rare classes from 31.2% to 44.6%. Moreover, combining the OWL-ST recipe with the OWLv2 architecture achieves new state-of-the-art performance.

Overall, the OWL-ST recipe presented in this paper significantly improves detection performance by leveraging weak supervision from large-scale web data, enabling web-scale training for open-world localization. This approach addresses the limitations posed by the scarcity of labeled detection data and demonstrates the potential for achieving robust open-vocabulary object detection in a scalable manner.

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