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Tool-based reasoning in IQA-T1 leads to significant improvements in image quality assessments, outperforming traditional methods while enhancing interpretability.
Enforcing spatio-temporal structural consistency in event-based optical flow estimation yields state-of-the-art performance, even surpassing traditional frame-based methods.
By distilling visual foundation models, this work achieves a significant leap in event stream representation learning, surpassing prior methods in generalization, data efficiency, and transferability.
