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iFAN boosts segmentation accuracy by aligning query competition with mask quality, achieving significant performance gains without extra computational overhead.
Relying on a single cleanliness score can lead to compounding errors in noisy-label learning, but TRACE uncouples the assessment of observed and pseudo labels for more reliable supervision.
Textual shortcuts in VLMs can significantly undermine reasoning accuracy, but a simple intervention can restore fresh visual computation.
Explicitly modeling object orientation can drastically reduce systematic errors in multimodal spatial reasoning, outperforming traditional methods by a significant margin.
