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