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🏢 University of Texas at Dallas

STONE: A Submodular Optimization Framework for Active 3D Object Detection
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AI Generated Computer Vision 3D Vision 🏢 University of Texas at Dallas
STONE: A novel submodular optimization framework drastically cuts 3D object detection training costs by cleverly selecting the most informative LiDAR point cloud data for labeling, achieving state-of-…
SEL-BALD: Deep Bayesian Active Learning for Selective Labeling with Instance Rejection
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Machine Learning Active Learning 🏢 University of Texas at Dallas
SEL-BALD tackles the challenge of human discretion in active learning by proposing novel algorithms that account for instance rejection, significantly boosting sample efficiency.
IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering
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AI Generated Natural Language Processing Question Answering 🏢 University of Texas at Dallas
IQA-EVAL: An automatic evaluation framework uses LLMs to simulate human-AI interactions and evaluate interactive question answering, achieving high correlation with human judgments.
Continual Audio-Visual Sound Separation
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Multimodal Learning Audio-Visual Learning 🏢 University of Texas at Dallas
ContAV-Sep: a novel approach to continual audio-visual sound separation, effectively mitigating catastrophic forgetting and improving model adaptability by preserving cross-modal semantic similarity a…
A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models
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AI Theory Optimization 🏢 University of Texas at Dallas
A novel neural network efficiently answers arbitrary Most Probable Explanation (MPE) queries in large probabilistic models, eliminating the need for slow inference algorithms.