Search Results for author:
Found 5 papers, 1 papers with code
SkipViT: Speeding Up Vision Transformers with a Token-Level Skip Connection
Our method does not add any parameters to the ViT model and aims to find the best trade-off between training throughput and achieving a 0% loss in the Top-1 accuracy of the final model.
Test-Time Personalization with Meta Prompt for Gaze Estimation
Despite the recent remarkable achievement in gaze estimation, efficient and accurate personalization of gaze estimation without labels is a practical problem but rarely touched on in the literature.
Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization
However, their application to chart-related tasks is not trivial as these tasks typically involve considering not only the underlying data but also the visual features in the chart image.
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of training.
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
We tested our method on ViT, which achieved an approximate 0. 3% increase in accuracy while reducing the model size by about 4% in the task of image classification.
