We introduce LightGlue, a deep neural network that learns to match local features across
images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse …

Recent advances in Transformer-based large language models (LLMs) have led to
significant performance improvements across many tasks. These gains come with a drastic …

Multi-agent collaborative perception (MCP) has recently attracted much attention. It includes
three key processes: communication for sharing, collaboration for integration, and …

Modern predictive models are often deployed to environments in which computational
budgets are dynamic. Anytime algorithms are well-suited to such environments as, at any …

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is
a popular vision-based approach. While deep neural networks have shown impressive …

Multi-Exit models (MEMs) use an early-exit strategy to improve the accuracy and efficiency of
deep neural networks (DNNs) by allowing samples to exit the network before the last layer …

Well-trained deep neural networks (DNNs) treat all test samples equally during prediction.
Adaptive DNN inference with early exiting leverages the observation that some test …

Large language models (LLMs) have acquired the ability to handle longer context lengths
and understand nuances in text, expanding their dialogue capabilities beyond a single …

Recently, large language models (LLMs) have demonstrated superior performance across
various tasks by adhering to scaling laws, which significantly increase model size. However …

We consider semantic segmentation of images using deep neural networks. To reduce the
computational cost, we incorporate the idea of early exit, where different pixels can be …