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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 1–25 of 36 papers

TitleStatusHype
Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language ModelsCode2
λ-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent SpaceCode2
FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial InformationCode1
ConceptCLIP: Towards Trustworthy Medical AI via Concept-Enhanced Contrastive Langauge-Image Pre-trainingCode1
RadAlign: Advancing Radiology Report Generation with Vision-Language Concept AlignmentCode1
Text-Video Retrieval with Global-Local Semantic Consistent LearningCode1
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal ModelsCode1
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
AltDiffusion: A Multilingual Text-to-Image Diffusion ModelCode1
ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion ModelsCode1
Concept Extraction Using Pointer-Generator NetworksCode1
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
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
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
Concept Alignment—0
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