619 citations · 6.4k across the 178 of their papers we have counts for
21 papers · 1 filter
Robust LSTM-Autoencoders for Face De-Occlusion in the Wild
Fang Zhao, Jiashi Feng, Jian Zhao +2
Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers…
Video Scene Parsing with Predictive Feature Learning
Xiaojie Jin, Xin Li, Huaxin Xiao +9
In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we co…
Correlation Alignment for Unsupervised Domain Adaptation
Baochen Sun, Jiashi Feng, Kate Saenko
In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second-ord…
Deep Joint Rain Detection and Removal from a Single Image
Wenhan Yang, Robby T. Tan, Jiashi Feng +3
In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in the new rain image mode…
Multi-stage Object Detection with Group Recursive Learning
Jianan Li, Xiaodan Liang, Jianshu Li +3
Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately. However, the important…
Multi-Path Feedback Recurrent Neural Network for Scene Parsing
Xiaojie Jin, Yunpeng Chen, Jiashi Feng +2
In this paper, we consider the scene parsing problem and propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene images. MPF-RNN can enhance the ca…
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training
Ilija Ilievski, Jiashi Feng
Recently, several optimization methods have been successfully applied to the hyperparameter optimization of deep neural networks (DNNs). The methods work by modeling the joint dist…
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
Xiaojie Jin, Yunpeng Chen, Jian Dong +2
Intermediate features at different layers of a deep neural network are known to be discriminative for visual patterns of different complexities. However, most existing works ignore…
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods
Xiaojie Jin, Xiaotong Yuan, Jiashi Feng +1
Deep neural networks have achieved remarkable success in a wide range of practical problems. However, due to the inherent large parameter space, deep models are notoriously prone t…
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Ilija Ilievski, Taimoor Akhtar, Jiashi Feng +1
Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has be…
Diversified Visual Attention Networks for Fine-Grained Object Classification
Bo Zhao, Xiao Wu, Jiashi Feng +2
Fine-grained object classification is a challenging task due to the subtle inter-class difference and large intra-class variation. Recently, visual attention models have been appli…
End-to-End Comparative Attention Networks for Person Re-identification
Hao Liu, Jiashi Feng, Meibin Qi +2
Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem. One of the major challenges lies in the l…
Deep Q-Networks for Accelerating the Training of Deep Neural Networks
Jie Fu
In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our ap…
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization
Le Thi Khanh Hien, Cuong V. Nguyen, Huan Xu +2
We consider the problem of minimizing the sum of an average function of a large number of smooth convex components and a general, possibly non-differentiable, convex function. Alth…
A Focused Dynamic Attention Model for Visual Question Answering
Ilija Ilievski, Shuicheng Yan, Jiashi Feng
Visual Question and Answering (VQA) problems are attracting increasing interest from multiple research disciplines. Solving VQA problems requires techniques from both computer visi…
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
Wenhan Yang, Jiashi Feng, Jianchao Yang +4
In this work, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are impor…
Attentive Contexts for Object Detection
Jianan Li, Yunchao Wei, Xiaodan Liang +4
Modern deep neural network based object detection methods typically classify candidate proposals using their interior features. However, global and local surrounding contexts that…
Semantic Object Parsing with Graph LSTM
Xiaodan Liang, Xiaohui Shen, Jiashi Feng +2
By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LST…
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
Tom Zahavy, Bingyi Kang, Alex Sivak +3
The question why deep learning algorithms generalize so well has attracted increasing research interest. However, most of the well-established approaches, such as hypothesis capaci…
Scale-aware Pixel-wise Object Proposal Networks
Zequn Jie, Xiaodan Liang, Jiashi Feng +3
Object proposal is essential for current state-of-the-art object detection pipelines. However, the existing proposal methods generally fail in producing results with satisfying loc…
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks
Jie Fu, Hongyin Luo, Jiashi Feng +2
The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters. Recent advances in reverse-mode automatic differentiation allow for o…