571 citations · 3.6k across the 80 of their papers we have counts for
28 papers · 1 filter
PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection
Nian Liu, Junwei Han, Ming-Hsuan Yang
In saliency detection, every pixel needs contextual information to make saliency prediction. Previous models usually incorporate contexts holistically. However, for each pixel, usu…
A Fusion Approach for Multi-Frame Optical Flow Estimation
Zhile Ren, Orazio Gallo, Deqing Sun +3
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has…
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
Yibing Song, Jiawei Zhang, Lijun Gong +5
We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled…
Deep Attentive Tracking via Reciprocative Learning
Shi Pu, Yibing Song, Chao Ma +2
Visual attention, derived from cognitive neuroscience, facilitates human perception on the most pertinent subset of the sensory data. Recently, significant efforts have been made t…
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang +2
Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven f…
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Yijun Li, Chen Fang, Jimei Yang +3
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame. In this work, we study the problem of generating co…
Learning Linear Transformations for Fast Arbitrary Style Transfer
Xueting Li, Sifei Liu, Jan Kautz +1
Given a random pair of images, an arbitrary style transfer method extracts the feel from the reference image to synthesize an output based on the look of the other content image. R…
Physics-Based Generative Adversarial Models for Image Restoration and Beyond
Jinshan Pan, Jiangxin Dong, Yang Liu +5
We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed,…
Learning Blind Video Temporal Consistency
Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang +3
Applying image processing algorithms independently to each frame of a video often leads to undesired inconsistent results over time. Developing temporally consistent video-based ex…
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Xinyi Zhang, Hang Dong, Zhe Hu +3
Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pi…
Superpixel Sampling Networks
Varun Jampani, Deqing Sun, Ming-Yu Liu +2
Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. Existing superpixel a…
Fast and Accurate Online Video Object Segmentation via Tracking Parts
Jingchun Cheng, Yi-Hsuan Tsai, Wei-Chih Hung +2
Online video object segmentation is a challenging task as it entails to process the image sequence timely and accurately. To segment a target object through the video, numerous CNN…
Learning to Deblur Images with Exemplars
Jinshan Pan, Wenqi Ren, Zhe Hu +1
Human faces are one interesting object class with numerous applications. While significant progress has been made in the generic deblurring problem, existing methods are less effec…
Learning Dual Convolutional Neural Networks for Low-Level Vision
Jinshan Pan, Sifei Liu, Deqing Sun +8
In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and deha…
Correlation Tracking via Joint Discrimination and Reliability Learning
Chong Sun, Dong Wang, Huchuan Lu +1
For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually f…
VITAL: VIsual Tracking via Adversarial Learning
Yibing Song, Chao Ma, Xiaohe Wu +6
The tracking-by-detection framework consists of two stages, i.e., drawing samples around the target object in the first stage and classifying each sample as the target object or as…
Learning a Discriminative Prior for Blind Image Deblurring
Lerenhan Li, Jinshan Pan, Wei-Sheng Lai +3
We present an effective blind image deblurring method based on a data-driven discriminative prior.Our work is motivated by the fact that a good image prior should favor clear image…
Simultaneous Fidelity and Regularization Learning for Image Restoration
Dongwei Ren, Wangmeng Zuo, David Zhang +2
Most existing non-blind restoration methods are based on the assumption that a precise degradation model is known. As the degradation process can only be partially known or inaccur…
Gated Fusion Network for Single Image Dehazing
Wenqi Ren, Lin Ma, Jiawei Zhang +4
In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that c…
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Feng Li, Cheng Tian, Wangmeng Zuo +2
Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to resolve…
Deep Semantic Face Deblurring
Ziyi Shen, Wei-Sheng Lai, Tingfa Xu +2
In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs). As face images are highly…
Learning to Localize Sound Source in Visual Scenes
Arda Senocak, Tae-Hyun Oh, Junsik Kim +2
Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize…
Learning to Adapt Structured Output Space for Semantic Segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter +3
Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As t…
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Hang Su, Varun Jampani, Deqing Sun +4
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.…