VL-AcneSeg: A Vision-Language Framework for Region-Aware Acne Lesion Segmentation

arXiv:2609.34472 · cs.CV, cs.AI · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/sukjuoh/VL-AcneSeg

Terminology

• Transformer — 21× in this paper · explained in 2 episodes
• Embeddings — 14× in this paper · explained in 2 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Accuracy — 8× in this paper · explained in 1 episode
• Ablation — 7× in this paper · explained in 1 episode
• nnU-Net — 6× in this paper · explained in 2 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• External Validation — 2× in this paper · explained in 2 episodes
• Prompt Engineering — 2× in this paper · explained in 4 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Prompting — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Layer Normalization — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
• DescriptorMedSAM: Language-Image Fusion with Multi-Aspect Text Guidance for Medical Image Segmentation
• MediaPipe: A Framework for Building Perception Pipelines
• Decoupled Weight Decay Regularization
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• Can CNNs Be More Robust Than Transformers?

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