66 citations · 86 across the 6 of their papers we have counts for
7 papers
Selective Structured State Space for Multispectral-fused Small Target Detection
Qianqian Zhang, WeiJun Wang, Yunxing Liu +4
Target detection in high-resolution remote sensing imagery faces challenges due to the low recognition accuracy of small targets and high computational costs. The computational com…
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
Shuyang Sun, Weijun Wang, Qihang Yu +3
This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment. We observe that due to its hi…
MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded Context
Weijun Wang, Andrew Howard
We present a next-generation neural network architecture, MOSAIC, for efficient and accurate semantic image segmentation on mobile devices. MOSAIC is designed using commonly suppor…
Discovering Multi-Hardware Mobile Models via Architecture Search
Grace Chu, Okan Arikan, Gabriel Bender +7
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another impor…
FoodX-251: A Dataset for Fine-grained Food Classification
Parneet Kaur, Karan Sikka, Weijun Wang +2
Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training sta…
Geo-Aware Networks for Fine-Grained Recognition
Grace Chu, Brian Potetz, Weijun Wang +5
Fine-grained recognition distinguishes among categories with subtle visual differences. In order to differentiate between these challenging visual categories, it is helpful to leve…
Searching for MobileNetV3
Andrew Howard, Mark Sandler, Grace Chu +9
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone…