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The computer vision problem that you are describing is object detection, i.e. the problem of finding the location of specific objects in an image and label them correctly with their names. There are many resources on the web (or in books) that describe this problem more in detail and examples (which also include code) to get you started with it (e.g. this ...


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Due to subjective nature, quantitative evaluation of synthetic images is difficult in general. However, there are metrics like Inception Score or FID score that are used for evaluation of generative models like GANs or VAEs. Technically, it considers two aspects of the generated data: Similarity with training data Diversity within itself Even though such ...


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The most generic answer to this question is: the same metrics you use to evaluate the quality of your model during training or in test phase. (Plus the timing of inference if you're referring to computational efficiency) And I'm not referring to any specific metric yet cause that's really task dependent. But in general if you have a model that perform a task ...


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Well, I suppose one can use some kind of contrastive learning in this case. A famous example of the establishment of relation between two different representations is the CLIP - Contrastive Language–Image Pre-training, where model gets a huge corpus of image captions and images and the image caption is passed through the language model, and the image itself ...


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