323 citations · 1.4k across the 42 of their papers we have counts for
8 papers · 1 filter
Slimmable Neural Networks
Jiahui Yu, Linjie Yang, Ning Xu +2
We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-eff…
Deep neural network based i-vector mapping for speaker verification using short utterances
Jinxi Guo, Ning Xu, Kailun Qian +4
Text-independent speaker recognition using short utterances is a highly challenging task due to the large variation and content mismatch between short utterances. I-vector based sy…
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
Ning Xu, Linjie Yang, Yuchen Fan +4
Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentatio…
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
Ning Xu, Linjie Yang, Yuchen Fan +6
Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentatio…
Wide Activation for Efficient and Accurate Image Super-Resolution
Jiahui Yu, Yuchen Fan, Jianchao Yang +4
In this report we demonstrate that with same parameters and computational budgets, models with wider features before ReLU activation have significantly better performance for singl…
Factorized Adversarial Networks for Unsupervised Domain Adaptation
Jian Ren, Jianchao Yang, Ning Xu +1
In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks. Our networks map the data distribut…
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
Jian Ren, Zhe Li, Jianchao Yang +3
Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In…
Learn to Combine Modalities in Multimodal Deep Learning
Kuan Liu, Yanen Li, Ning Xu +1
Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches. However, it is challenging to full…