Scaling Open-Vocabulary Object Detection
Open-vocabulary object detection has benefited greatly from pretrained
vision-language models, but is still limited by the amount of available
detection training data. While detection training data can be expanded by using
Web image-text pairs as weak supervision, this has not been done at scales
comparable to image-level pretraining. Here, we scale up detection data with
self-training, which uses an existing detector to generate pseudo-box
annotations on image-text pairs. Major challenges in scaling self-training are
the choice of label space, pseudo-annotation filtering, and training
efficiency. We present the OWLv2 model and OWL-ST self-training recipe, which
address these challenges. OWLv2 surpasses the performance of previous
state-of-the-art open-vocabulary detectors already at comparable training
scales (~10M examples). However, with OWL-ST, we can scale to over 1B examples,
yielding further large improvement: With an L/14 architecture, OWL-ST improves
AP on LVIS rare classes, for which the model has seen no human box annotations,
from 31.2% to 44.6% (43% relative improvement). OWL-ST unlocks Web-scale
training for open-world localization, similar to what has been seen for image
classification and language modelling.
648fbbf6544876b8efd42600
Matthias Minderer
claimed_verified
2023-06-27T12:55:32.028Z
649ac6e57c36fc2dc6e6b0f4
https://cdn-avatars.huggingface.co/v1/production/uploads/649ac6e57c36fc2dc6e6b0f4/Scd3LiYW5Y2qunDDHnPWw.jpeg
Matthias Minderer
user
mjlm
648fbbf6544876b8efd42601
Alexey Gritsenko
admin_assigned
2023-06-19T11:42:43.405Z
62d8f9887b8dc0ba17271415
/avatars/12ec78d34fd849bad44217b212f31e98.svg
Alexey Gritsenko
user
AlexeyG
648fbbf6544876b8efd42602
Neil Houlsby
claimed_verified
2023-07-03T15:23:01.179Z
64a2c7f263f69fb98d3bfdb4
/avatars/a362a236c0654b7605dcb7673e309335.svg
Neil Houlsby
user
neilhoulsby
Scaling Open-Vocabulary Object Detection
Open-vocabulary object detection has benefited greatly from pretrained
vision-language models, but is still limited by the amount of available
detection training data. While detection training data can be expanded by using
Web image-text pairs as weak supervision, this has not been done at scales
comparable to image-level pretraining. Here, we scale up detection data with
self-training, which uses an existing detector to generate pseudo-box
annotations on image-text pairs. Major challenges in scaling self-training are
the choice of label space, pseudo-annotation filtering, and training
efficiency. We present the OWLv2 model and OWL-ST self-training recipe, which
address these challenges. OWLv2 surpasses the performance of previous
state-of-the-art open-vocabulary detectors already at comparable training
scales (~10M examples). However, with OWL-ST, we can scale to over 1B examples,
yielding further large improvement: With an L/14 architecture, OWL-ST improves
AP on LVIS rare classes, for which the model has seen no human box annotations,
from 31.2% to 44.6% (43% relative improvement). OWL-ST unlocks Web-scale
training for open-world localization, similar to what has been seen for image
classification and language modelling.
OWL-ST self-training enhances OWLv2 model's open-vocabulary object detection performance by scaling up to over 1B examples, significantly improving rare class detection.
self-training pseudo-box annotations label space pseudo-annotation filtering training efficiency OWLv2 OWL-ST AP LVIS rare classes Web-scale training