MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
Fine-tunes SegFormer-B0 and B1 on FoodSeg103 for ingredient segmentation, reporting mIoU of 0.2521 and 0.3204, then derives ingredient area percentages for nutrition awareness.
citing papers explorer
-
MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
-
Towards Migrating Neural Network Implementations
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
-
Ingredient-Level Food Image Segmentation for Nutrition Awareness
Fine-tunes SegFormer-B0 and B1 on FoodSeg103 for ingredient segmentation, reporting mIoU of 0.2521 and 0.3204, then derives ingredient area percentages for nutrition awareness.