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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 1–10 of 36 papers

TitleStatusHype
Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language ModelsCode2
FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial InformationCode1
Replace in Translation: Boost Concept Alignment in Counterfactual Text-to-Image—0
An Explanation of Intrinsic Self-Correction via Linear Representations and Latent Concepts—0
Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin—0
Training-free Dense-Aligned Diffusion Guidance for Modular Conditional Image SynthesisCode0
Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning ModelsCode0
Interpretable Concept-based Deep Learning Framework for Multimodal Human Behavior Modeling—0
ConceptCLIP: Towards Trustworthy Medical AI via Concept-Enhanced Contrastive Langauge-Image Pre-trainingCode1
RadAlign: Advancing Radiology Report Generation with Vision-Language Concept AlignmentCode1
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