Year: 2025
Type: article
Abstract: The existing spatial non cooperative target datasets only focus on single modality, and there are problems with the inability to effectively reflect fine-grained positional relationships and the inability to train multimodal models. This article constructs the first multimodal intent recognition dataset for non cooperative spatial targets, exploring the paradigm of using large models to generate multimodal intent data for spatial targets and feedback model retraining. Based on DeepseekV3 and ChatGPT 4o, detailed image text correspondence and composite intent simulation of fine-grained spatial target position relationships were conducted through prompt design, enhanced feedback, and compliance control. 11000 pairs of noisy image text data were constructed for 72 segmented scenarios. Based on this dataset, fine tune and train the multimodal model CLIP and the unimodal model Resnet separately. The experimental results show that multimodal data can improve intent recognition accuracy by 4.47%. This dataset provides a foundation for the training and evaluation of multimodal models in spatial target intent recognition, driving the model towards training modes of image text and ″human model″ interaction, and promoting the development of large-scale models in this field.
Source: China Scientific Data
Authors: Tian MA, Chuyang SHANG, Wanzhu REN, Shaofeng MA, Hanwen ZHANG
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117137679