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Found 5 papers, 2 papers with code
Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuning
Specifically, to enhance the representation abilities of vision and language encoders, we propose the Multi-level Reconstruction Pre-training (MR-Pretrain) strategy, including a feature-level and data-level reconstruction, which guides models to capture the semantic information from masked inputs of different modalities.
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
MRM: Masked Relation Modeling for Medical Image Pre-Training with Genetics
Moreover, to enhance semantic relation modeling, we propose relation matching to align the sample-wise relation between the intact and masked features.
Towards Robust Adaptive Object Detection under Noisy Annotations
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate the target labels with the given target domain images.
