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Found 8 papers, 2 papers with code
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-rays
The model is developed using a step-by-step approach, starting with being pretrained on natural images and texts, followed by being trained using longitudinal chest X-ray data.
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model
Visual question answering (VQA) is a task where an image is given, and a series of questions are asked about the image.
Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization
In the FACT method, we meta-trained a neural network and a hash-encoder using a few scans (= 15), and a new regularization technique is utilized to reconstruct the details of an anatomical structure.
Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy Sources
In this paper, we first propose a method for defining anomalies considering not only individual energy sources but also correlations between them.
PePe: Personalized Post-editing Model utilizing User-generated Post-edits
Incorporating personal preference is crucial in advanced machine translation tasks.
Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity
To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences.
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated.
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.
