StrLoRA is a regularized two-stage expert routing method for streaming CVIT that selects experts via textual instructions and applies token-wise cross-modal weighting with historical routing alignment.
Places: A 10 million image database for scene recognition
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HIL-CBM is a hierarchical label-free concept bottleneck model that improves classification accuracy and explanation quality over prior single-level CBMs using a visual consistency loss and dual heads.
citing papers explorer
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StrLoRA: Towards Streaming Continual Visual Instruction Tuning for MLLMs
StrLoRA is a regularized two-stage expert routing method for streaming CVIT that selects experts via textual instructions and applies token-wise cross-modal weighting with historical routing alignment.
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Hierarchical, Interpretable, Label-Free Concept Bottleneck Model
HIL-CBM is a hierarchical label-free concept bottleneck model that improves classification accuracy and explanation quality over prior single-level CBMs using a visual consistency loss and dual heads.