  A critical object detection task is finetuning an existing model to detect
novel objects, but the standard workflow requires bounding box annotations
which are time-consuming and expensive to collect. Weakly supervised object
detection (WSOD) offers an appealing alternative, where object detectors can be
trained using image-level labels. However, the practical application of current
WSOD models is limited, as they only operate at small data scales and require
multiple rounds of training and refinement. To address this, we propose the
Weakly Supervised Detection Transformer, which enables efficient knowledge
transfer from a large-scale pretraining dataset to WSOD finetuning on hundreds
of novel objects. Additionally, we leverage pretrained knowledge to improve the
multiple instance learning (MIL) framework often used in WSOD methods. Our
experiments show that our approach outperforms previous state-of-the-art models
on large-scale novel object detection datasets, and our scaling study reveals
that class quantity is more important than image quantity for WSOD pretraining.
The code is available at https://github.com/tmlabonte/weakly-supervised-DETR.
