181 citations · 276 across the 12 of their papers we have counts for
19 papers
Omni-Dimensional Dynamic Convolution
Chao Li, Aojun Zhou, Anbang Yao
Learning a single static convolutional kernel in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research i…
Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks
Yikai Wang, Yi Yang, Fuchun Sun +1
In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the…
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
Yikai Wang, Fuchun Sun, Ming Lu +1
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, u…
AccSS3D: Accelerator for Spatially Sparse 3D DNNs
Om Ji Omer, Prashant Laddha, Gurpreet S Kalsi +6
Semantic understanding and completion of real world scenes is a foundational primitive of 3D Visual perception widely used in high-level applications such as robotics, medical imag…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Knowledge Transfer via Dense Cross-Layer Mutual-Distillation
Anbang Yao, Dawei Sun
Knowledge Distillation (KD) based methods adopt the one-way Knowledge Transfer (KT) scheme in which training a lower-capacity student network is guided by a pre-trained high-capaci…