Cross-View Supervision transfers geometric and topological priors from ego-aligned overhead perspectives into camera-based BEV encoders via feature-space alignment, yielding up to 44% relative mAP gains at long range on nuScenes.
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T-DuMpRa fuses classifier outputs with cosine-matched multi-prototypes from a teacher model via conservative gating, yielding 0.21-2.69% gains on skin lesion datasets across five backbones.
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Learning Ego-Centric BEV Representations from a Perspective-Privileged View: Cross-View Supervision for Online HD Map Construction
Cross-View Supervision transfers geometric and topological priors from ego-aligned overhead perspectives into camera-based BEV encoders via feature-space alignment, yielding up to 44% relative mAP gains at long range on nuScenes.
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T-DuMpRa: Teacher-guided Dual-path Multi-prototype Retrieval Augmented framework for fine-grained medical image classification
T-DuMpRa fuses classifier outputs with cosine-matched multi-prototypes from a teacher model via conservative gating, yielding 0.21-2.69% gains on skin lesion datasets across five backbones.