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<!-- HTML_TAG_START --><p><a rel="noopener nofollow" href="https://arxiv.org/abs/2112.03857">"<strong>GLIP: Grounded Language-Image Pre-training. CVPR 2022, Best Paper Finalist</strong>"</a></p>
This is the HuggingFace Gradio Demo for GLIP. The model requires an image, and a text to be the inputs. The text input can either be a natural sentence description (grounding), or a simple concatenation of some random categories (object detection).
The 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.
Code: https://github.com/microsoft/GLIP
News: We are also holding an ODinW challenge at the CV in the Wild Workshop @ ECCV 2022. We hope our open-source code encourage the community to participate in this challenge!
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