1 code implementation • 11 Dec 2023 • Abdullah Rashwan, Jiageng Zhang, Ali Taalimi, Fan Yang, Xingyi Zhou, Chaochao Yan, Liang-Chieh Chen, Yeqing Li
With ResNet50 backbone, our MaskConver achieves 53. 6% PQ on the COCO panoptic val set, outperforming the modern convolution-based model, Panoptic FCN, by 9. 3% as well as transformer-based models such as Mask2Former (+1. 7% PQ) and kMaX-DeepLab (+0. 6% PQ).
Ranked #8 on Panoptic Segmentation on COCO test-dev
no code implementations • 28 Sep 2023 • Ke Yu, Stephen Albro, Giulia Desalvo, Suraj Kothawade, Abdullah Rashwan, Sasan Tavakkol, Kayhan Batmanghelich, Xiaoqi Yin
Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure.
1 code implementation • ICCV 2023 • Tianlong Chen, Xuxi Chen, Xianzhi Du, Abdullah Rashwan, Fan Yang, Huizhong Chen, Zhangyang Wang, Yeqing Li
Instead of compressing multiple tasks' knowledge into a single model, MoE separates the parameter space and only utilizes the relevant model pieces given task type and its input, which provides stabilized MTL training and ultra-efficient inference.
no code implementations • 23 Mar 2021 • Abdullah Rashwan, Xianzhi Du, Xiaoqi Yin, Jing Li
Scale-permuted networks have shown promising results on object bounding box detection and instance segmentation.
Ranked #5 on Semantic Segmentation on PASCAL VOC 2012 val
no code implementations • 25 Feb 2020 • Amur Ghose, Abdullah Rashwan, Pascal Poupart
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input.
1 code implementation • 9 Jan 2020 • Abdullah Rashwan, Rishav Agarwal, Agastya Kalra, Pascal Poupart
We present MatrixNets (xNets), a new deep architecture for object detection.
2 code implementations • 13 Aug 2019 • Abdullah Rashwan, Agastya Kalra, Pascal Poupart
We present Matrix Nets (xNets), a new deep architecture for object detection.
Ranked #108 on Object Detection on COCO test-dev
no code implementations • NeurIPS 2018 • Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable.