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Concept Alignment

Concept Alignment aims to align the learned representations or concepts within a model with the intended or target concepts. It involves adjusting the model's parameters or training process to ensure that the learned concepts accurately reflect the underlying patterns in the data.

Papers

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Showing 11–20 of 36 papers

TitleStatusHype
Text-Video Retrieval with Global-Local Semantic Consistent LearningCode1
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic GraphsCode0
Improving Concept Alignment in Vision-Language Concept Bottleneck ModelsCode0
A Self-explaining Neural Architecture for Generalizable Concept LearningCode0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis—0
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal ModelsCode1
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples—0
SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection—0
λ-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent SpaceCode2
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
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