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Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity attention operator for more efficient token routing, but they require from-scratch pre-training and typically underperform the original Softmax version. How to initialize linear ViTs both efficiently and e...
This work proposes CADE (Contrastive Adaptive Debias Ensemble), a training-free, plug-and-play method that leverages modality-specific answer priors that yields significant gains on the proposed benchmark, which can foster the development of more fair and reliable AI systems for sustainable development.
DiD is introduced, a label-free conversion method that exclusively trains the linear-attention backbone by aligning detector-facing interface tensors with those of a frozen Softmax teacher, and substantially outperforms established baselines and matches supervised, fully trained linear models.
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