most citedExploring Diverse Methods in Visual Question Answering

52 citations · 111 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025★ 3 cited

Data Augmentation Through Random Style Replacement

Qikai Yang, Cheng Ji, Huaiying Luo +2

In this paper, we introduce a novel data augmentation technique that combines the advantages of style augmentation and random erasing by selectively replacing image subregions with…

cs.CV2024★ 8 cited

Enhance Image-to-Image Generation with LLaVA-generated Prompts

Zhicheng Ding, Panfeng Li, Qikai Yang +1

This paper presents a novel approach to enhance image-to-image generation by leveraging the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We propose a…

cs.CV2024★ 27 cited

Regional Style and Color Transfer

Zhicheng Ding, Panfeng Li, Qikai Yang +2

This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire…

cs.SI2024★ 21 cited

A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation

Qikai Yang, Panfeng Li, Xinhe Xu +3

In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development…

cs.CV2024★ 52 cited

Exploring Diverse Methods in Visual Question Answering

Panfeng Li, Qikai Yang, Xieming Geng +3

This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. Leveragin…