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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 26–36 of 36 papers

TitleStatusHype
NEUCORE: Neural Concept Reasoning for Composed Image Retrieval—0
Replace in Translation: Boost Concept Alignment in Counterfactual Text-to-Image—0
SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection—0
A Self-explaining Neural Architecture for Generalizable Concept LearningCode0
Training-free Dense-Aligned Diffusion Guidance for Modular Conditional Image SynthesisCode0
CapEnrich: Enriching Caption Semantics for Web Images via Cross-modal Pre-trained KnowledgeCode0
Joint covariate-alignment and concept-alignment: a framework for domain generalizationCode0
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic GraphsCode0
Improving Concept Alignment in Vision-Language Concept Bottleneck ModelsCode0
Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning ModelsCode0
Discovery of Natural Language Concepts in Individual Units of CNNsCode0
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