most citedA Study of Situational Reasoning for Traffic Understanding

8 citations · 21 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20233 cited

Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems

Alessandro Oltramari

High-level reasoning can be defined as the capability to generalize over knowledge acquired via experience, and to exhibit robust behavior in novel situations. Such form of reasoni…

cs.CV20232 cited

Traffic-Domain Video Question Answering with Automatic Captioning

Ehsan Qasemi, Jonathan M. Francis, Alessandro Oltramari

Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and Inte…

cs.CL20238 cited

A Study of Situational Reasoning for Traffic Understanding

Jiarui Zhang, Filip Ilievski, Kaixin Ma +3

Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road safety/security and for enabling smart city infrastructure. Understanding traffic situation…

cs.AI20221 cited

Intelligent Traffic Monitoring with Hybrid AI

Ehsan Qasemi, Alessandro Oltramari

Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and modalities of data and the need for the utilization of state-of-the-art (SOTA) reasone…

cs.CL20227 cited

Coalescing Global and Local Information for Procedural Text Understanding

Kaixin Ma, Filip Ilievski, Jonathan Francis +2

Procedural text understanding is a challenging language reasoning task that requires models to track entity states across the development of a narrative. A complete procedural unde…