Feb. 13, 2024, 5:48 a.m. | Serdar Erisen

cs.CV updates on arXiv.org arxiv.org

Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient residual network. Attention-boosting gates (AbGs) and attention-boosting modules (AbMs) are deployed by aiming to fuse the feature-based semantic information with the global context of the …

art attention boosting cnns computational convolutional neural networks cost cs.ai cs.cv efficiency fusion gates global information network networks neural networks research residual segmentation semantic state success

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Engineering Director-Big Data technologies (Hadoop, Spark, Hive, Kafka)

@ Visa | Bengaluru, India

Senior Data Engineer

@ Manulife | Makati City, Manulife Philippines Head Office

GDS Consulting Senior Data Scientist 2

@ EY | Taguig, PH, 1634

IT Data Analyst Team Lead

@ Rosecrance | Rockford, Illinois, United States