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A unified benchmark reveals the trade-offs between pixel-wise accuracy and perceptual realism in state-of-the-art image super-resolution techniques.
You can slash the text encoder size in vision-language segmentation models by 88% without sacrificing performance, thanks to surprisingly high redundancy in how these models process prompts.
