Berkin Akin — accepted AI-conference papers

2024

MobileNetV4: Universal Models for the Mobile Ecosystem

Danfeng Qin*, Chas H Leichner, Manolis Delakis, Marco Fornoni, Shixin Luo, Fan Yang +8

ECCV 2024oral

"We present the latest generation of MobileNets: MobileNetV4 (MNv4). They feature universally-efficient architecture designs for mobile devices. We introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexible structure that merges Inverted Bottleneck (IB), ConvNext, Feed Fo…

Cited by 193 Source ↗PDF ↗code ↗Scholar ↗

2021

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang +4

CVPR 2021poster

Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting…

Cited by 189 PDF ↗code ↗Scholar ↗