13 research outputs found
ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?
ChatGPT embarks on a new era of artificial intelligence and will
revolutionize the way we approach intelligent traffic safety systems. This
paper begins with a brief introduction about the development of large language
models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety
issues. Furthermore, we discuss the controversies surrounding LLMs, raise
critical questions for their deployment, and provide our solutions. Moreover,
we propose an idea of multi-modality representation learning for smarter
traffic safety decision-making and open more questions for application
improvement. We believe that LLM will both shape and potentially facilitate
components of traffic safety research.Comment: Submitted to Nature - Machine Intelligence (Revised and Extended
Predicate correlation learning for scene graph generation
For a typical Scene Graph Generation (SGG) method, there is often a large gap
in the performance of the predicates' head classes and tail classes. This
phenomenon is mainly caused by the semantic overlap between different
predicates as well as the long-tailed data distribution. In this paper, a
Predicate Correlation Learning (PCL) method for SGG is proposed to address the
above two problems by taking the correlation between predicates into
consideration. To describe the semantic overlap between strong-correlated
predicate classes, a Predicate Correlation Matrix (PCM) is defined to quantify
the relationship between predicate pairs, which is dynamically updated to
remove the matrix's long-tailed bias. In addition, PCM is integrated into a
Predicate Correlation Loss function () to reduce discouraging gradients
of unannotated classes. The proposed method is evaluated on Visual Genome
benchmark, where the performance of the tail classes is significantly improved
when built on the existing methods