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About this paper
Jialian Wu, Jianfeng Wang, Zhengyuan Yang, Zhe Gan, Zicheng Liu, Junsong Yuan, Lijuan Wang
GRiT is introduced as a generative region-to-text transformer that redefines object understanding as an open-set, generative task rather than a classification problem. The model generates rich textual descriptions for detected objects, achieving 60.4 AP on COCO 2017 test-dev for object detection and state-of-the-art 15.5 mAP on Visual Genome for dense captioning.
Key insights
- Reconceptualizing object understanding as a generative language task allows for open-set recognition and detailed textual descriptions.
- A single generative framework can effectively unify object detection and dense captioning through task-specific control tokens.
- Joint training of detection and language modeling capabilities in a generative model yields competitive performance across benchmarks.
Summary by alphaXiv.
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