82 citations · 135 across the 6 of their papers we have counts for
6 papers
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
Yingwei Li, Adams Wei Yu, Tianjian Meng +10
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While prevalent multi-modal methods simply decorate raw lidar…
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation
Tianjian Meng, Golnaz Ghiasi, Reza Mahjourian +2
It is commonly believed that high internal resolution combined with expensive operations (e.g. atrous convolutions) are necessary for accurate semantic segmentation, resulting in s…
A Design Space Study for LISTA and Beyond
Tianjian Meng, Xiaohan Chen, Yifan Jiang +1
In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling…
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Yanqi Zhou, Xuanyi Dong, Berkin Akin +7
Neural architectures and hardware accelerators have been two driving forces for the progress in deep learning. Previous works typically attempt to optimize hardware given a fixed m…
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou, Mao Ye, Chen Chen +6
For deploying a deep learning model into production, it needs to be both accurate and compact to meet the latency and memory constraints. This usually results in a network that is…
Spatiotemporal Contrastive Video Representation Learning
Rui Qian, Tianjian Meng, Boqing Gong +4
We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are l…