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Depth integration in audio-visual segmentation leads to over 10% performance gains, revealing a critical yet overlooked modality in multimodal perception.
The shift towards unified correspondence models in cross-view feature matching could redefine benchmarks and performance standards in vision tasks.
Target-Guided Selective Reweighting PINN achieves superior parameter recovery in inverse problems without sacrificing field accuracy, even amidst challenging source-target mismatches.
