Deep Learning (DL) models have been widely deployed on IoT devices with the help of
advancements in DL algorithms and chips. However, the limited resources of edge devices …

Over the last decade there has been considerable research into how artificial intelligence
(AI), specifically computer vision, can assist people who are blind or have low-vision (BLV) …

Learning the human--mobility interaction (HMI) on interactive scenes (eg, how a vehicle
turns at an intersection in response to traffic lights and other oncoming vehicles) can …

Large language models (LLMs) have revolutionized natural language processing with their
exceptional capabilities. However, deploying LLMs on resource-constrained edge devices …

The limited capabilities of user equipment restrict the local implementation of computation-
intensive applications. Edge computing, especially the edge intelligence system, enables …

The Large Language Model (LLM) has gained significant popularity and is extensively
utilized across various domains. Most LLM deployments occur within cloud data centers …

Traditional machine learning models often require powerful hardware, making them
unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has …

Over the last decade, there has been significant research on how smart assistive devices
with artificial intelligence (AI) built into them can assist people with severe vision …

Visual Question Answering (VQA) lies at the intersection of vision and language domains
necessitating learning representations from multiple modalities. While the model …

Real-time aerial image segmentation plays an important role in the environmental
perception of Uncrewed Aerial Vehicles (UAVs). We introduce BlabberSeg, an optimized …