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GRiT: A Generative Region-to-text Transformer for Object Understanding

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arxiv 2212.00280 v1 pith:7GFOWEDV submitted 2022-12-01 cs.CV

Jialian Wu , Jianfeng Wang , Zhengyuan Yang , Zhe Gan , Zicheng Liu , Junsong Yuan , Lijuan Wang This is my paper · ORCID
classification cs.CV
keywords objectgritobjectstextcaptioningdensedetectionunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a Generative RegIon-to-Text transformer, GRiT, for object understanding. The spirit of GRiT is to formulate object understanding as <region, text> pairs, where region locates objects and text describes objects. For example, the text in object detection denotes class names while that in dense captioning refers to descriptive sentences. Specifically, GRiT consists of a visual encoder to extract image features, a foreground object extractor to localize objects, and a text decoder to generate open-set object descriptions. With the same model architecture, GRiT can understand objects via not only simple nouns, but also rich descriptive sentences including object attributes or actions. Experimentally, we apply GRiT to object detection and dense captioning tasks. GRiT achieves 60.4 AP on COCO 2017 test-dev for object detection and 15.5 mAP on Visual Genome for dense captioning. Code is available at https://github.com/JialianW/GRiT

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