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When transferring our pretrained model to the open-vocabulary object detection task, our method outperforms the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and LVIS datasets, respec- tively. Further, the learned region representations support zero-shot inference...
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The inference throughput is measured on Nvidia V100 with CUDA 11 and PyTorch v1.12 [40], where the batch size is 1,024. 4.2. Comparison with State-of-the-Art Models We compare our TinyCLIP with state-of-the-art models in Tab. 1. The zero-shot evalu...