April 16, 2024, 4:47 a.m. | Yang Hu, Jinxia Zhang, Kaihua Zhang, Yin Yuan

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.08936v1 Announce Type: new
Abstract: Efficient and accurate camouflaged object detection (COD) poses a challenge in the field of computer vision. Recent approaches explored the utility of edge information for network co-supervision, achieving notable advancements. However, these approaches introduce an extra branch for complex edge extraction, complicate the model architecture and increases computational demands. Addressing this issue, our work replicates the effect that animal's camouflage can be easily revealed under a shifting spotlight, and leverages it for network co-supervision to …

abstract arxiv challenge computer computer vision cs.cv detection edge extra extraction however information network object simple spotlight supervision through type utility vision

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Data Scientist

@ ITE Management | New York City, United States