Multi-Modal Classifiers for Open-Vocabulary Object Detection
The goal of this paper is open-vocabulary object detection (OVOD)
x2013 building a model that can detect objects beyond the set of
categories seen at training, thus enabling the user to specify categories of
interest at inference without the need for model retraining. We adopt a
standard two-stage object detector architecture, and explore three ways for
specifying novel categories: via language descriptions, via image exemplars, or
via a combination of the two. We make three contributions: first, we prompt a
large language model (LLM) to generate informative language descriptions for
object classes, and construct powerful text-based classifiers; second, we
employ a visual aggregator on image exemplars that can ingest any number of
images as input, forming vision-based classifiers; and third, we provide a
simple method to fuse information from language descriptions and image
exemplars, yielding a multi-modal classifier. When evaluating on the
challenging LVIS open-vocabulary benchmark we demonstrate that: (i) our
text-based classifiers outperform all previous OVOD works; (ii) our
vision-based classifiers perform as well as text-based classifiers in prior
work; (iii) using multi-modal classifiers perform better than either modality
alone; and finally, (iv) our text-based and multi-modal classifiers yield
better performance than a fully-supervised detector.
648682986a2a8eca9d1ea69c
Prannay Kaul
claimed_verified
2023-06-12T18:59:18.187Z
63500f69eaf46b3c3d893c1c
/avatars/c2a776c6239eb229a6fbdc490f8d86ec.svg
Prannay Kaul
user
prannaykaul
648682986a2a8eca9d1ea69d
Weidi Xie
admin_assigned
2023-06-12T10:04:01.923Z
63f1841f2f7c0152e8748ec4
/avatars/5398658c7d0cf556531a625a4ca5d18a.svg
Xie
user
Weidi
648682986a2a8eca9d1ea69e
Andrew Zisserman
Multi-Modal Classifiers for Open-Vocabulary Object Detection
The goal of this paper is open-vocabulary object detection (OVOD)
x2013 building a model that can detect objects beyond the set of
categories seen at training, thus enabling the user to specify categories of
interest at inference without the need for model retraining. We adopt a
standard two-stage object detector architecture, and explore three ways for
specifying novel categories: via language descriptions, via image exemplars, or
via a combination of the two. We make three contributions: first, we prompt a
large language model (LLM) to generate informative language descriptions for
object classes, and construct powerful text-based classifiers; second, we
employ a visual aggregator on image exemplars that can ingest any number of
images as input, forming vision-based classifiers; and third, we provide a
simple method to fuse information from language descriptions and image
exemplars, yielding a multi-modal classifier. When evaluating on the
challenging LVIS open-vocabulary benchmark we demonstrate that: (i) our
text-based classifiers outperform all previous OVOD works; (ii) our
vision-based classifiers perform as well as text-based classifiers in prior
work; (iii) using multi-modal classifiers perform better than either modality
alone; and finally, (iv) our text-based and multi-modal classifiers yield
better performance than a fully-supervised detector.
A novel open-vocabulary object detection method uses language descriptions and image exemplars to detect objects unseen during training, achieving superior performance in multi-modal classification.
open-vocabulary object detection two-stage object detector large language model text-based classifiers visual aggregator vision-based classifiers multi-modal classifier LVIS benchmark fully-supervised detector