4 accepted papers
Collecting high-quality question-answer (QA) pairs is vital for the training of large language models (LLMs), yet this process is traditionally laborious and time-intensive. With the rapid evolution of LLMs, the potential for leveraging these models to autonomously generate QA pairs has become appar…
Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation ability for extracting informative features from input images. However, the mainstream transformer models are designed wi…
Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural im…
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization. This paper delves deep into the working mechanism, and reveals that AutoAugme…