  This paper presents a grounded language-image pre-training (GLIP) model for
learning object-level, language-aware, and semantic-rich visual
representations. GLIP unifies object detection and phrase grounding for
pre-training. The unification brings two benefits: 1) it allows GLIP to learn
from both detection and grounding data to improve both tasks and bootstrap a
good grounding model; 2) GLIP can leverage massive image-text pairs by
generating grounding boxes in a self-training fashion, making the learned
representation semantic-rich. In our experiments, we pre-train GLIP on 27M
grounding data, including 3M human-annotated and 24M web-crawled image-text
pairs. The learned representations demonstrate strong zero-shot and few-shot
transferability to various object-level recognition tasks. 1) When directly
evaluated on COCO and LVIS (without seeing any images in COCO during
pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many
supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val
and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13
downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised
Dynamic Head. Code is released at https://github.com/microsoft/GLIP.
