28 citations · 93 across the 18 of their papers we have counts for
19 papers
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation
Linfeng Zhang, Xin Chen, Xiaobing Tu +3
Remarkable achievements have been attained with Generative Adversarial Networks (GANs) in image-to-image translation. However, due to a tremendous amount of parameters, state-of-th…
CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
Haitian Zheng, Zhe Lin, Jingwan Lu +7
Recent image inpainting methods have made great progress but often struggle to generate plausible image structures when dealing with large holes in complex images. This is partiall…
Adaptive Cross-Layer Attention for Image Restoration
Yancheng Wang, Ning Xu, Yingzhen Yang
Non-local attention module has been proven to be crucial for image restoration. Conventional non-local attention processes features of each layer separately, so it risks missing co…
Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective
Yuxi Li, Ning Xu, Wenjie Yang +2
Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still sho…
TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation
Jinyu Yang, Jingjing Liu, Ning Xu +1
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a labeled source domain to an unlabeled target domain. Previous work is mainly built upon convolutio…
Semantic Layout Manipulation with High-Resolution Sparse Attention
Haitian Zheng, Zhe Lin, Jingwan Lu +4
We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transf…
Mask Guided Matting via Progressive Refinement Network
Qihang Yu, Jianming Zhang, He Zhang +5
We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matti…
Finding Action Tubes with a Sparse-to-Dense Framework
Yuxi Li, Weiyao Lin, Tao Wang +5
The task of spatial-temporal action detection has attracted increasing attention among researchers. Existing dominant methods solve this problem by relying on short-term informatio…
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Yuxi Li, Weiyao Lin, John See +4
Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is explo…
Video Question Answering on Screencast Tutorials
Wentian Zhao, Seokhwan Kim, Ning Xu +1
This paper presents a new video question answering task on screencast tutorials. We introduce a dataset including question, answer and context triples from the tutorial videos for…
Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation
Kenan E. Ak, Ning Xu, Zhe Lin +1
Autoregressive models recently achieved comparable results versus state-of-the-art Generative Adversarial Networks (GANs) with the help of Vector Quantized Variational AutoEncoders…
Multiple Sound Sources Localization from Coarse to Fine
Rui Qian, Di Hu, Heinrich Dinkel +3
How to visually localize multiple sound sources in unconstrained videos is a formidable problem, especially when lack of the pairwise sound-object annotations. To solve this proble…
AOWS: Adaptive and optimal network width search with latency constraints
Maxim Berman, Leonid Pishchulin, Ning Xu +2
Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the targ…
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results
Seungjun Nah, Sanghyun Son, Radu Timofte +1
Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challeng…
Minimizing FLOPs to Learn Efficient Sparse Representations
Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen +3
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a la…
Getting to 99% Accuracy in Interactive Segmentation
Marco Forte, Brian Price, Scott Cohen +2
Interactive object cutout tools are the cornerstone of the image editing workflow. Recent deep-learning based interactive segmentation algorithms have made significant progress in…
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention
Donghyun Kim, Tian Lan, Chuhang Zou +5
Prior work in multi-task learning has mainly focused on predictions on a single image. In this work, we present a new approach for multi-task learning from videos via efficient int…
Video Instance Segmentation
Linjie Yang, Yuchen Fan, Ning Xu
In this paper we present a new computer vision task, named video instance segmentation. The goal of this new task is simultaneous detection, segmentation and tracking of instances…
Learning D-FilterMap for Deep Convolutional Neural Networks
Yingzhen Yang, Jianchao Yang, Ning Xu +1
We present a novel and compact architecture for deep Convolutional Neural Networks (CNNs) in this paper, termed D-FilterMap Convolutional Neural Networks (D-FM-CNNs). The con…