1.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Electronic Science and Technology of ChinaCN28 papers
- Shanghai Jiao Tong UniversityCN25 papers
- Stockholm UniversitySE21 papers
- Beijing Computational Science Research CenterCN19 papers
- University of the Basque CountryES19 papers
- IkerbasqueES18 papers
- National Key Laboratory of Science and Technology on CommunicationsCN17 papers
- Zhejiang UniversityCN17 papers
- Columbia UniversityUS15 papers
- City University of New YorkUS12 papers
- Massachusetts Institute of TechnologyUS12 papers
- University of California, BerkeleyUS12 papers
34 papers · 1 filter
Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction
Bo Sun, Hao Kang, Li Guan +3
We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. P…
VIPeR: Visual Incremental Place Recognition with Adaptive Mining and Continual Learning
Yuhang Ming, Minyang Xu, Xingrui Yang +5
Visual place recognition (VPR) is an essential component of many autonomous and augmented/virtual reality systems. It enables the systems to robustly localize themselves in large-s…
Real-Time Pill Identification for the Visually Impaired Using Deep Learning
Bo Dang, Wenchao Zhao, Yufeng Li +3
The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the de…
Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation
Hao Wang, Jiayou Qin, Xiwen Chen +4
Assistive visual navigation systems for visually impaired individuals have become increasingly popular thanks to the rise of mobile computing. Most of these devices work by transla…
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model
Caiyang Yu, Xianggen Liu, Yifan Wang +5
Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have ach…
Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset
Necdet Gurkan, Jordan W. Suchow
Face detection is a long-standing challenge in the field of computer vision, with the ultimate goal being to accurately localize human faces in an unconstrained environment. There…
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
Yongcheng Jing, Yiding Yang, Xinchao Wang +2
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both th…
Single-Camera 3D Head Fitting for Mixed Reality Clinical Applications
Tejas Mane, Aylar Bayramova, Kostas Daniilidis +2
We address the problem of estimating the shape of a person's head, defined as the geometry of the complete head surface, from a video taken with a single moving camera, and determi…
Cross-View Exocentric to Egocentric Video Synthesis
Gaowen Liu, Hao Tang, Hugo Latapie +2
Cross-view video synthesis task seeks to generate video sequences of one view from another dramatically different view. In this paper, we investigate the exocentric (third-person)…
VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals
Zhixiang Min, Yiding Yang, Enrique Dunn
We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem a…
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks
Zihan Li, Chen Li, Yudong Yao +7
Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (…
Do End-to-end Stereo Algorithms Under-utilize Information?
Changjiang Cai, Philippos Mordohai
Deep networks for stereo matching typically leverage 2D or 3D convolutional encoder-decoder architectures to aggregate cost and regularize the cost volume for accurate disparity es…
Matching-space Stereo Networks for Cross-domain Generalization
Changjiang Cai, Matteo Poggi, Stefano Mattoccia +1
End-to-end deep networks represent the state of the art for stereo matching. While excelling on images framing environments similar to the training set, major drops in accuracy occ…
Learning Propagation Rules for Attribution Map Generation
Yiding Yang, Jiayan Qiu, Mingli Song +2
Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the…
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
Weihong Ren, Xinchao Wang, Jiandong Tian +2
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object de…
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
Tian Han, Erik Nijkamp, Linqi Zhou +3
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). The joint training of VAE and latent EBM are b…
Disassembling Object Representations without Labels
Zunlei Feng, Xinchao Wang, Yongming He +3
In this paper, we study a new representation-learning task, which we termed as disassembling object representations. Given an image featuring multiple objects, the goal of disassem…
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
Matteo Poggi, Fabio Tosi, Konstantinos Batsos +2
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local,…
Learning Oracle Attention for High-fidelity Face Completion
Tong Zhou, Changxing Ding, Shaowen Lin +2
High-fidelity face completion is a challenging task due to the rich and subtle facial textures involved. What makes it more complicated is the correlations between different facial…
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Jingwen Ye, Yixin Ji, Xinchao Wang +2
Recent advances in deep learning have provided procedures for learning one network to amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural Network (CN…
Distilling Knowledge from Graph Convolutional Networks
Yiding Yang, Jiayan Qiu, Mingli Song +2
Existing knowledge distillation methods focus on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph c…
Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers
Ya Zhao, Rui Xu, Xinchao Wang +3
Lip reading has witnessed unparalleled development in recent years thanks to deep learning and the availability of large-scale datasets. Despite the encouraging results achieved, t…
Dynamic Instance Normalization for Arbitrary Style Transfer
Yongcheng Jing, Xiao Liu, Yukang Ding +4
Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way. Such manually-defi…
Deep Model Transferability from Attribution Maps
Jie Song, Yixin Chen, Xinchao Wang +2
Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so a…