665 citations · 2.1k across the 57 of their papers we have counts for
9 papers · 1 filter
Are Fewer Labels Possible for Few-shot Learning?
Suichan Li, Dongdong Chen, Yinpeng Chen +4
Few-shot learning is challenging due to its very limited data and labels. Recent studies in big transfer (BiT) show that few-shot learning can greatly benefit from pretraining on l…
MiniVLM: A Smaller and Faster Vision-Language Model
Jianfeng Wang, Xiaowei Hu, Pengchuan Zhang +5
Recent vision-language (VL) studies have shown remarkable progress by learning generic representations from massive image-text pairs with transformer models and then fine-tuning on…
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
Wei He, Quanming Yao, Chao Li +4
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising, compressed…
MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network
Yi Wei, Zhe Gan, Wenbo Li +5
We present Mask-guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to…
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning
Shengjie Liu, Qian Shi, Liangpei Zhang
Current hyperspectral image classification assumes that a predefined classification system is closed and complete, and there are no unknown or novel classes in the unseen data. How…
VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning
Xiaowei Hu, Xi Yin, Kevin Lin +4
It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated…
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices
Xin Cheng, Lei Zhang, Yin Tang +3
Recently, deep learning has represented an important research trend in human activity recognition (HAR). In particular, deep convolutional neural networks (CNNs) have achieved stat…
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors
Yin Tang, Qi Teng, Lei Zhang +2
Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various human activity recognition (HAR) datasets. However, deep CNNs often require more computin…
Sequential Weakly Labeled Multi-Activity Localization and Recognition on Wearable Sensors using Recurrent Attention Networks
Kun Wang, Jun He, Lei Zhang
With the popularity and development of the wearable devices such as smartphones, human activity recognition (HAR) based on sensors has become as a key research area in human comput…