model = OFAModel.from_pretrained(ckpt_dir, use_cache=False) gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3) print(tokenizer.batch_decode(gen, skip_special_tokens=True)) 零样本下的结果: 对比两个模型生成的描述: BLIP: a photography of a boy i...
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可以看到,模型可以分为三个板块,其中ITC表示“image-text contrative”,用来对齐视觉和语言表示;ITM表示“image-text matching”,使用交叉注意力层来模拟图文信息交互,来区分正负图像-文本对;LM表示“language model”,用causal注意力代替双向注意力机制,并且与编码器共享参数,用来生成图片描述。作者将这种结构称作MED(mu...
“For example, an image classification model will tell you that a dog, grass and a Frisbee are in the image,” Google noted, “But a natural description should also tell you the color of the grass and how the dog relates to the Frisbee.” While you may not need Google to tell you w...