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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
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial InformationCode1
Improving Concept Alignment in Vision-Language Concept Bottleneck ModelsCode0
Joint covariate-alignment and concept-alignment: a framework for domain generalizationCode0
Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning ModelsCode0
A Self-explaining Neural Architecture for Generalizable Concept LearningCode0
Training-free Dense-Aligned Diffusion Guidance for Modular Conditional Image SynthesisCode0
Discovery of Natural Language Concepts in Individual Units of CNNsCode0
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic GraphsCode0
CapEnrich: Enriching Caption Semantics for Web Images via Cross-modal Pre-trained KnowledgeCode0
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