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Found 14 papers, 6 papers with code
Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractorwith an Explanation Decoder
We introduce a method that transforms a rule-based relation extraction (RE) classifier into a neural one such that both interpretability and performance are achieved.
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.
The 7th AI City Challenge
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.
It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers
Our approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier.
The 6th AI City Challenge
The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries.
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks
The general goal of text simplification (TS) is to reduce text complexity for human consumption.
Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder
Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier.
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention.
Eidos, INDRA, \& Delphi: From Free Text to Executable Causal Models
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking.
MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D
Multiple object tracking has been a challenging field, mainly due to noisy detection sets and identity switch caused by occlusion and similar appearance among nearby targets.
Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration
In this challenge, we propose a model-based vehicle localization method, which builds a kernel at each patch of the 3D deformable vehicle model and associates them with constraints in 3D space.
