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Achieve more robust and informative visual explanations for CNNs by adaptively fusing gradient-based and region-based CAM methods, outperforming existing approaches on standard benchmarks.
Forget bigger models: smarter training objectives like pairwise MarginMSE and listwise InfoNCE can boost cross-encoder performance as much as scaling the backbone architecture.
Cross-encoders can be made 4x faster with minimal performance loss by surgically removing interactions, creating a "minimal interaction" architecture (MICE) that rivals late-interaction models in speed and surpasses them in generalization.
