Integrating Query-aware Segmentation and Cross-Attention for Robust VQA

Autor:	Choi, Wonjun, Lee, Sangbeom, Lee, Seungyeon, Jung, Heechul, Lee, Dong-Gyu
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	This paper introduces a method for VizWiz-VQA using LVLM with trainable cross-attention and LoRA finetuning. We train the model with the following conditions: 1) Training with original images. 2) Training with enhanced images using CLIPSeg to highlight or contrast the original image. 3) Training with integrating the output features of Vision Transformer (ViT) and CLIPSeg features of the original images. Then, we ensemble the results based on Levenshtein distance to enhance the prediction of the final answer. In the experiments, we demonstrate and analyze the proposed method's effectiveness. Comment: CVPR Workshop accepted, Vizwiz Grand Challenge(VQA) 3rd Prize, https://vizwiz.cs.colorado.edu/VizWiz_workshop/abstracts/choi_2024_vizwiz_CVPR.pdf
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.12055 Zobrazit plný text záznamu View this record from Arxiv