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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
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis—0
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples—0
3D Point Cloud Pre-training with Knowledge Distillation from 2D Images—0
Grammar-Based Concept Alignment for Domain-Specific Machine Translation—0
Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin—0
Interpretable Concept-based Deep Learning Framework for Multimodal Human Behavior Modeling—0
Language-based Action Concept Spaces Improve Video Self-Supervised Learning—0
Natural Language Detectors Emerge in Individual Neurons—0
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
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