Vision language models in autonomous driving and intelligent transportation systems
The applications of Vision-Language Models (VLMs) in the fields of Autonomous Driving
(AD) and Intelligent Transportation Systems (ITS) have attracted widespread attention due to …
(AD) and Intelligent Transportation Systems (ITS) have attracted widespread attention due to …
Vision language models in autonomous driving: A survey and outlook
The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD)
have attracted widespread attention due to their outstanding performance and the ability to …
have attracted widespread attention due to their outstanding performance and the ability to …
[PDF][PDF] V2V: Efficiently Synthesizing Video Results for Video Queries
D Winecki, A Nandi - 2024 IEEE 40th International Conference on …, 2024 - ixlab.github.io
Querying video data has become increasingly popular and useful. Video queries can be
complex, ranging from retrieval tasks (“find me the top videos that have...”), to analytics (“how …
complex, ranging from retrieval tasks (“find me the top videos that have...”), to analytics (“how …
Self-Enhancing Video Data Management System for Compositional Events with Large Language Models [Technical Report]
Complex video queries can be answered by decomposing them into modular subtasks.
However, existing video data management systems assume the existence of predefined …
However, existing video data management systems assume the existence of predefined …
Radar Spectra-Language Model for Automotive Scene Parsing
Radar sensors are low cost, long-range, and weather-resilient. Therefore, they are widely
used for driver assistance functions, and are expected to be crucial for the success of …
used for driver assistance functions, and are expected to be crucial for the success of …
Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learning
A Ziai, A Vartakavi - arXiv preprint arXiv:2402.06560, 2024 - arxiv.org
High-quality and consistent annotations are fundamental to the successful development of
robust machine learning models. Traditional data annotation methods are resource …
robust machine learning models. Traditional data annotation methods are resource …