2024
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning
Du Y, Chang B, Dvornek N. CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning. Lecture Notes In Computer Science 2024, 15012: 465-475. DOI: 10.1007/978-3-031-72390-2_44.Peer-Reviewed Original ResearchContrastive Language-Image Pre-trainingLanguage modelState-of-the-art performanceSelf-supervised representation learningContrastive learning methodFine-tuningProlonged training timeBERT encoderContrastive learningRepresentation learningClass labelsGPU resourcesTraining samplesTraining timeMammography datasetModel sizePre-trainingLearning methodsEfficient frameworkVisual modelRichness of informationDatasetClinical diagnostic dataLearningMedical applications
2019
ShelfNet for Fast Semantic Segmentation
Zhuang J, Yang J, Gu L, Dvornek N. ShelfNet for Fast Semantic Segmentation. 2019, 00: 847-856. DOI: 10.1109/iccvw.2019.00113.Peer-Reviewed Original ResearchFast semantic segmentationSemantic segmentationCityscapes datasetReal-time segmentation modelStreet-scene understandingFast inference speedEncoder-decoder structurePASCAL VOC datasetHigh accuracyInference speedSkip connectionsAutonomous drivingExtensive experimentsResidual blocksSegmentation modelVOC datasetNovel architectureReal-time methodComputation burdenShallow pathsBiSeNetPSPNetParameter numberComparable speedDataset