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University of Technology Sydney
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Selective visual-text compression in SEER boosts extraction precision while slashing token usage, outperforming leading models in long-context reasoning tasks.
A single generalist model can achieve state-of-the-art performance in Referring Expression Comprehension while generalizing across diverse datasets without fine-tuning.
