Target-Driven Visual Navigation by Using Causal Intervention

Zhao, Xinzhou; Wang, Tian; Li, Yanjing; Zhang, Baochang; Liu, Kexin; Liu, Deyuan; Wang, Chuanyun; Snoussi, Hichem

doi:10.1109/tiv.2023.3288810

Cited by 2 publications

(1 citation statement)

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“…The advent of deep learning has revolutionized the landscape of visual navigation [23,24]. Contemporary approaches leverage the prowess of neural networks to learn complex mappings between visual inputs and navigational outputs.…”

Section: Deep Learning Models For Visual Navigationmentioning

confidence: 99%

FLsM: Fuzzy Localization of Image Scenes Based on Large Models

Chen,

Miao,

Gui

et al. 2024

Electronics

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This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in image processing, particularly in developing visual navigation and localization techniques using large-scale visual models. This paper introduces a sophisticated scene image localization technique based on large models in a vast spatial sample environment. The study involved training convolutional neural networks using millions of geographically labeled images, extracting image position information using large model algorithms, and collecting sample data under various conditions in elastic scene space. Through visual computation, the shooting position of photos was inferred to obtain the approximate position information of users. This method utilizes geographic location information to classify images and combines it with landmarks, natural features, and architectural styles to determine their locations. The experimental results show variations in positioning accuracy among different models, with the most optimal model obtained through training on a large-scale dataset. They also indicate that the positioning error in urban street-based images is relatively small, whereas the positioning effect in outdoor and local scenes, especially in large-scale spatial environments, is limited. This suggests that the location information of users can be effectively determined through the utilization of geographic data, to classify images and incorporate landmarks, natural features, and architectural styles. The study’s experimentation indicates the variation in positioning accuracy among different models, highlighting the significance of training on a large-scale dataset for optimal results. Furthermore, it highlights the contrasting impact on urban street-based images versus outdoor and local scenes in large-scale spatial environments.

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Section: Deep Learning Models For Visual Navigationmentioning

confidence: 99%