1.8k citations · 3.2k across the 15 of their papers we have counts for
16 papers
DetailCLIP: Injecting Image Details into CLIP's Feature Space
Zilun Zhang, Cuifeng Shen, Yuan Shen +4
Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e.g., 224),…
Feature Space Singularity for Out-of-Distribution Detection
Haiwen Huang, Zhihan Li, Lulu Wang +3
Out-of-Distribution (OoD) detection is important for building safe artificial intelligence systems. However, current OoD detection methods still cannot meet the performance require…
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
Delong Chen, Shunhui Ji, Fan Liu +2
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time…
Component-wise Adaptive Trimming For Robust Mixture Regression
Wennan Chang, Xinyu Zhou, Yong Zang +2
Parameter estimation of mixture regression model using the expectation maximization (EM) algorithm is highly sensitive to outliers. Here we propose a fast and efficient robust mixt…
DPGN: Distribution Propagation Graph Network for Few-shot Learning
Ling Yang, Liangliang Li, Zilun Zhang +3
Most graph-network-based meta-learning approaches model instance-level relation of examples. We extend this idea further to explicitly model the distribution-level relation of one…
Learning Delicate Local Representations for Multi-Person Pose Estimation
Yuanhao Cai, Zhicheng Wang, Zhengxiong Luo +7
In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delica…
Learning to Run with Actor-Critic Ensemble
Zhewei Huang, Shuchang Zhou, BoEr Zhuang +1
We introduce an Actor-Critic Ensemble(ACE) method for improving the performance of Deep Deterministic Policy Gradient(DDPG) algorithm. At inference time, our method uses a critic e…
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin +1
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MF…
EAST: An Efficient and Accurate Scene Text Detector
Xinyu Zhou, Cong Yao, He Wen +4
Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging…
Training Bit Fully Convolutional Network for Fast Semantic Segmentation
He Wen, Shuchang Zhou, Zhe Liang +4
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires l…
Effective Quantization Methods for Recurrent Neural Networks
Qinyao He, He Wen, Shuchang Zhou +4
Reducing bit-widths of weights, activations, and gradients of a Neural Network can shrink its storage size and memory usage, and also allow for faster training and inference by exp…
Scene Text Detection via Holistic, Multi-Channel Prediction
Cong Yao, Xiang Bai, Nong Sang +3
Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge. However, vas…
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni +3
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients. In particular, during…
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
Shuchang Zhou, Jia-Nan Wu, Yuxin Wu +1
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks. We use Kronecker product to exploit the lo…
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Cong Yao, Jianan Wu, Xinyu Zhou +4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability…
ICDAR 2015 Text Reading in the Wild Competition
Xinyu Zhou, Shuchang Zhou, Cong Yao +2
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community. However, maj…