1 code implementation • 17 Aug 2021 • Ionut Cosmin Duta, Mariana Iuliana Georgescu, Radu Tudor Ionescu
On the one hand, we integrate CoConv in the widely-used residual networks and show improved recognition performance over baselines on the core tasks and benchmarks for visual recognition, namely image classification on the ImageNet data set and object detection on the MS COCO data set.
Ranked #121 on Image Generation on CIFAR-10
3 code implementations • 20 Jun 2020 • Ionut Cosmin Duta, Li Liu, Fan Zhu, Ling Shao
This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales.
Ranked #69 on Semantic Segmentation on ADE20K val
2 code implementations • 10 Apr 2020 • Ionut Cosmin Duta, Li Liu, Fan Zhu, Ling Shao
We successfully train a 404-layer deep CNN on the ImageNet dataset and a 3002-layer network on CIFAR-10 and CIFAR-100, while the baseline is not able to converge at such extreme depths.
no code implementations • CVPR 2017 • Ionut Cosmin Duta, Bogdan Ionescu, Kiyoharu Aizawa, Nicu Sebe
The proposed method addresses an important problem of video understanding: how to build a video representation that incorporates the CNN features over the entire video.
Action Recognition In Videos Temporal Action Localization +1