STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models
Vision-language models (VLMs) have emerged as powerful tools for enabling automated traffic analysis; however, current approaches often demand substantial computational resources and struggle with fine-grained spatio-temporal understanding. This paper introduces STER-VLM, a computationally efficient framework that enhances VLM performance through (1) caption decomposition to tackle spatial and temporal information separately, (2) temporal frame selection with best-view filtering for sufficient temporal information, and (3) reference-driven understanding for capturing fine-grained motion and dynamic context and (4) curated visual/textual prompt techniques. Experimental results on the WTS \cite{kong2024wts} and BDD \cite{BDD} datasets demonstrate substantial gains in semantic richness and traffic scene interpretation. Our framework is validated through a decent test score of 55.655 in the AI City Challenge 2025 Track 2, showing its effectiveness in advancing resource-efficient and accurate traffic analysis for real-world applications.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change signif…
AttributeClick-Through Rate PredictionEnhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework
Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems. Despite the significant success which has been…
Rain RemovalVideo derainingL-STEC: Learned Video Compression with Long-term Spatio-Temporal Enhanced Context
Neural Video Compression has emerged in recent years, with condition-based frameworks outperforming traditional codecs. However, most existing methods rely solely on the previous frame's features to predict temporal cont…
Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding
Nowadays, navigation and ride-sharing apps have collected numerous images with spatio-temporal data. A core technology for analyzing such images, associated with spatiotemporal information, is Traffic Scene Understanding…
Scene UnderstandingFew-Shot LearningStrefer: Empowering Video LLMs with Space-Time Referring and Reasoning via Synthetic Instruction Data
Next-generation AI companions must go beyond general video understanding to resolve spatial and temporal references in dynamic, real-world environments. Existing Video Large Language Models (Video LLMs), while capable of…