10.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of MichiganUS1.5k papers
- Michigan State UniversityUS1.5k papers
- Centre National de la Recherche ScientifiqueFR1.4k papers
- Lawrence Berkeley National LaboratoryUS1.4k papers
- McGill UniversityCA1.4k papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR1.3k papers
- University of ManchesterGB1.3k papers
- University of LiverpoolGB1.3k papers
- University of TorontoCA1.3k papers
- University of OxfordGB1.3k papers
- TRIUMFCA1.3k papers
- Argonne National LaboratoryUS1.3k papers
26 papers · 2 filters
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Mohammad Akbari, Jie Liang, Jingning Han +1
Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rat…
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
Akshay Gadi Patil, Manyi Li, Matthew Fisher +2
We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout m…
SFU-Store-Nav: A Multimodal Dataset for Indoor Human Navigation
Zhitian Zhang, Jimin Rhim, Taher Ahmadi +3
This article describes a dataset collected in a set of experiments that involves human participants and a robot. The set of experiments was conducted in the computing science robot…
AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
Mahesh Kumar Krishna Reddy, Mrigank Rochan, Yiwei Lu +1
We address the problem of image-based crowd counting. In particular, we propose a new problem called unlabeled scene-adaptive crowd counting. Given a new target scene, we would lik…
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
Or Patashnik, Dov Danon, Hao Zhang +1
State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupe…
Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation
Haram Kim, Pyojin Kim, H. Jin Kim
Detection of moving objects is an essential capability in dealing with dynamic environments. Most moving object detection algorithms have been designed for color images without dep…
Unsupervised Single-Image Reflection Separation Using Perceptual Deep Image Priors
Suhong Kim, Hamed RahmaniKhezri, Seyed Mohammad Nourbakhsh +1
Reflections often degrade the quality of the image by obstructing the background scene. This is not desirable for everyday users, and it negatively impacts the performance of multi…
COALESCE: Component Assembly by Learning to Synthesize Connections
Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri +3
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections. To handle geometric and topolo…
Hierarchical Action Classification with Network Pruning
Mahdi Davoodikakhki, KangKang Yin
Research on human action classification has made significant progresses in the past few years. Most deep learning methods focus on improving performance by adding more network comp…
3D Human Shape Reconstruction from a Polarization Image
Shihao Zou, Xinxin Zuo, Yiming Qian +4
This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images. Polarization images are known to be able to…
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network
Hao Xu, Ka Hei Hui, Chi-Wing Fu +1
We introduce the first neural optimization framework to solve a classical instance of the tiling problem. Namely, we seek a non-periodic tiling of an arbitrary 2D shape using one o…
PIE-NET: Parametric Inference of Point Cloud Edges
Xiaogang Wang, Yuelang Xu, Kai Xu +4
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data. We represent these edges as a collection of parametric curves (i.e.,lines,…
Predictive and Generative Neural Networks for Object Functionality
Ruizhen Hu, Zihao Yan, Jingwen Zhang +4
Humans can predict the functionality of an object even without any surroundings, since their knowledge and experience would allow them to "hallucinate" the interaction or usage sce…
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
Zihao Yan, Ruizhen Hu, Xingguang Yan +4
We introduce RPM-Net, a deep learning-based approach which simultaneously infers movable parts and hallucinates their motions from a single, un-segmented, and possibly partial, 3D…
Self-Supervised Human Depth Estimation from Monocular Videos
Feitong Tan, Hao Zhu, Zhaopeng Cui +3
Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data. This paper presents a self-supervised method that can be train…
Graph2Plan: Learning Floorplan Generation from Layout Graphs
Ruizhen Hu, Zeyu Huang, Yuhan Tang +3
We introduce a learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human user…
LSM: Learning Subspace Minimization for Low-level Vision
Chengzhou Tang, Lu Yuan, Ping Tan
We study the energy minimization problem in low-level vision tasks from a novel perspective. We replace the heuristic regularization term with a learnable subspace constraint, and…
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
Kumar Abhishek, Ghassan Hamarneh, Mark S. Drew
The semantic segmentation of skin lesions is an important and common initial task in the computer aided diagnosis of dermoscopic images. Although deep learning-based approaches hav…
SAPIEN: A SimulAted Part-based Interactive ENvironment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo +11
Building home assistant robots has long been a pursuit for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, suff…
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
Nelson Nauata, Kai-Hung Chang, Chin-Yi Cheng +2
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode t…
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
Lei Li, Siyu Zhu, Hongbo Fu +2
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-…
Channel Equilibrium Networks for Learning Deep Representation
Wenqi Shao, Shitao Tang, Xingang Pan +3
Convolutional Neural Networks (CNNs) are typically constructed by stacking multiple building blocks, each of which contains a normalization layer such as batch normalization (BN) a…
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation
Wallace Lira, Johannes Merz, Daniel Ritchie +2
We introduce GANHopper, an unsupervised image-to-image translation network that transforms images gradually between two domains, through multiple hops. Instead of executing transla…
Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials
Min Li, Zhenglong Zhou, Zhe Wu +3
We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our…