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University of Sydney
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Ambiguous object selection in text-guided image editing is solved by directly annotating the canvas, achieving state-of-the-art performance in complex edits.
The tool-call rate can swing from zero to hundreds based solely on the interface used, revealing a hidden layer of performance that challenges conventional evaluations of model efficacy.
Over 1,500 submissions revealed stark differences in model performance across diverse domains, highlighting the challenges of generalizing egocentric video understanding.
A structured contract can boost HLS testbench pass rates by over 6% while integrating hardware feedback for real-time design improvements.