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Found 8 papers, 1 papers with code
Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model
We conclude that in the earlier generation stage, the image is mostly decided by the special token [\texttt{EOS}] in the text prompt, and the information in the text prompt is already conveyed in this stage.
Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference
Sparse-Tuning efficiently fine-tunes the pre-trained ViT by sparsely preserving the informative tokens and merging redundant ones, enabling the ViT to focus on the foreground while reducing computational costs on background regions in the images.
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey
Large-scale pre-trained vision models (PVMs) have shown great potential for adaptability across various downstream vision tasks.
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding
Extensive experiments on four dense scene understanding tasks demonstrate the superiority of VMT-Adapter(-Lite), achieving a 3. 96%(1. 34%) relative improvement compared to single-task full fine-tuning, while utilizing merely ~1% (0. 36%) trainable parameters of the pre-trained model.
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning
On the one hand, to maximize the complementarity of tasks with high similarity, we utilize a gradient-driven task grouping method that partitions tasks into several disjoint groups and assign a group-shared MmAP to each group.
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications.
Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT
252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT.
CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network
Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19.
