Search Results for author: Fan Zhu

Found 40 papers, 20 papers with code

Region Graph Embedding Network for Zero-Shot Learning

no code implementations ECCV 2020 Guo-Sen Xie, Li Liu, Fan Zhu, Fang Zhao, Zheng Zhang, Yazhou Yao, Jie Qin, Ling Shao

To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.

Graph Embedding Zero-Shot Learning

Visual-tactile sensing for Real-time liquid Volume Estimation in Grasping

no code implementations23 Feb 2022 Fan Zhu, Ruixing Jia, Lei Yang, Youcan Yan, Zheng Wang, Jia Pan, Wenping Wang

We propose a deep visuo-tactile model for realtime estimation of the liquid inside a deformable container in a proprioceptive way. We fuse two sensory modalities, i. e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor without any extra sensor calibrations. The robotic system is well controlled and adjusted based on the estimation model in real time.

Multi-Task Learning

G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation

no code implementations22 Jun 2021 Hao Huang, Boulbaba Ben Amor, Xichan Lin, Fan Zhu, Yi Fang

In this work, we introduce a joint geometric-neural networks approach for comparing, deforming and generating 3D protein structures.

Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment

no code implementations22 Jun 2021 Hao Huang, Boulbaba Ben Amor, Xichan Lin, Fan Zhu, Yi Fang

Our ResNet-TW (Deep Residual Network for Time Warping) tackles the alignment problem by compositing a flow of incremental diffeomorphic mappings.

Time Series Time Series Alignment +1

A LiDAR Assisted Control Module with High Precision in Parking Scenarios for Autonomous Driving Vehicle

no code implementations2 May 2021 Xin Xu, Yu Dong, Fan Zhu

For example, humans are good at interactive tasks (while autonomous driving systems usually do not), but we are often incompetent for tasks with strict precision demands.

Autonomous Driving

Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification

1 code implementation CVPR 2020 Yichao Yan, Jie Qin1, Jiaxin Chen, Li Liu, Fan Zhu, Ying Tai, Ling Shao

In each hypergraph, different temporal granularities are captured by hyperedges that connect a set of graph nodes (i. e., part-based features) across different temporal ranges.

Video-Based Person Re-Identification

Learning Multi-Attention Context Graph for Group-Based Re-Identification

1 code implementation29 Apr 2021 Yichao Yan, Jie Qin, Bingbing Ni, Jiaxin Chen, Li Liu, Fan Zhu, Wei-Shi Zheng, Xiaokang Yang, Ling Shao

Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks.

Person Re-Identification

Anchor-Free Person Search

1 code implementation CVPR 2021 Yichao Yan, Jinpeng Li, Jie Qin, Song Bai, Shengcai Liao, Li Liu, Fan Zhu, Ling Shao

Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id).

Pedestrian Detection Person Re-Identification +1

Group Whitening: Balancing Learning Efficiency and Representational Capacity

1 code implementation CVPR 2021 Lei Huang, Yi Zhou, Li Liu, Fan Zhu, Ling Shao

Results show that GW consistently improves the performance of different architectures, with absolute gains of $1. 02\%$ $\sim$ $1. 49\%$ in top-1 accuracy on ImageNet and $1. 82\%$ $\sim$ $3. 21\%$ in bounding box AP on COCO.

Normalization Techniques in Training DNNs: Methodology, Analysis and Application

no code implementations27 Sep 2020 Lei Huang, Jie Qin, Yi Zhou, Fan Zhu, Li Liu, Ling Shao

Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications.

Improved Residual Networks for Image and Video Recognition

2 code implementations10 Apr 2020 Ionut Cosmin Duta, Li Liu, Fan Zhu, Ling Shao

We successfully train a 404-layer deep CNN on the ImageNet dataset and a 3002-layer network on CIFAR-10 and CIFAR-100, while the baseline is not able to converge at such extreme depths.

Action Recognition Image Classification +2

Controllable Orthogonalization in Training DNNs

1 code implementation CVPR 2020 Lei Huang, Li Liu, Fan Zhu, Diwen Wan, Zehuan Yuan, Bo Li, Ling Shao

Orthogonality is widely used for training deep neural networks (DNNs) due to its ability to maintain all singular values of the Jacobian close to 1 and reduce redundancy in representation.

Image Classification

An Investigation into the Stochasticity of Batch Whitening

1 code implementation CVPR 2020 Lei Huang, Lei Zhao, Yi Zhou, Fan Zhu, Li Liu, Ling Shao

Our work originates from the observation that while various whitening transformations equivalently improve the conditioning, they show significantly different behaviors in discriminative scenarios and training Generative Adversarial Networks (GANs).

Auto-Encoding Twin-Bottleneck Hashing

1 code implementation CVPR 2020 Yuming Shen, Jie Qin, Jiaxin Chen, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao

One bottleneck (i. e., binary codes) conveys the high-level intrinsic data structure captured by the code-driven graph to the other (i. e., continuous variables for low-level detail information), which in turn propagates the updated network feedback for the encoder to learn more discriminative binary codes.

graph construction

Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs

no code implementations ECCV 2020 Lei Huang, Jie Qin, Li Liu, Fan Zhu, Ling Shao

To this end, we propose layer-wise conditioning analysis, which explores the optimization landscape with respect to each layer independently.

Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test

no code implementations NeurIPS 2019 Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong liu, Yu Li, Ling Shao

DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution.

Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent

no code implementations16 Sep 2019 Huan Xiong, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao

Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning.


Embarrassingly Simple Binary Representation Learning

1 code implementation26 Aug 2019 Yuming Shen, Jie Qin, Jiaxin Chen, Li Liu, Fan Zhu

Recent binary representation learning models usually require sophisticated binary optimization, similarity measure or even generative models as auxiliaries.

Representation Learning

RANet: Ranking Attention Network for Fast Video Object Segmentation

2 code implementations ICCV 2019 Ziqin Wang, Jun Xu, Li Liu, Fan Zhu, Ling Shao

Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner.

Frame online learning +3

NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising

1 code implementation17 Jun 2019 Yingkun Hou, Jun Xu, Mingxia Liu, Guanghai Liu, Li Liu, Fan Zhu, Ling Shao

This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance.

Image Denoising

Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image

1 code implementation17 Jun 2019 Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, Ling Shao

A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images.

Image Denoising

STAR: A Structure and Texture Aware Retinex Model

1 code implementation16 Jun 2019 Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, Ling Shao

A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.

Low-Light Image Enhancement

Towards Document Image Quality Assessment: A Text Line Based Framework and A Synthetic Text Line Image Dataset

no code implementations5 Jun 2019 Hongyu Li, Fan Zhu, Junhua Qiu

Firstly, since document image quality assessment is more interested in text, we propose a text line based framework to estimate document image quality, which is composed of three stages: text line detection, text line quality prediction, and overall quality assessment.

Image Quality Assessment Line Detection

iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images

3 code implementations30 May 2019 Syed Waqas Zamir, Aditya Arora, Akshita Gupta, Salman Khan, Guolei Sun, Fahad Shahbaz Khan, Fan Zhu, Ling Shao, Gui-Song Xia, Xiang Bai

Compared to existing small-scale aerial image based instance segmentation datasets, iSAID contains 15$\times$ the number of object categories and 5$\times$ the number of instances.

Instance Segmentation Object Detection +1

Dynamically Visual Disambiguation of Keyword-based Image Search

no code implementations27 May 2019 Yazhou Yao, Zeren Sun, Fumin Shen, Li Liu, Li-Min Wang, Fan Zhu, Lizhong Ding, Gangshan Wu, Ling Shao

To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.

General Classification Image Retrieval

Iterative Normalization: Beyond Standardization towards Efficient Whitening

5 code implementations CVPR 2019 Lei Huang, Yi Zhou, Fan Zhu, Li Liu, Ling Shao

With the support of SND, we provide natural explanations to several phenomena from the perspective of optimization, e. g., why group-wise whitening of DBN generally outperforms full-whitening and why the accuracy of BN degenerates with reduced batch sizes.

TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights

no code implementations ECCV 2018 Diwen Wan, Fumin Shen, Li Liu, Fan Zhu, Jie Qin, Ling Shao, Heng Tao Shen

Despite the remarkable success of Convolutional Neural Networks (CNNs) on generalized visual tasks, high computational and memory costs restrict their comprehensive applications on consumer electronics (e. g., portable or smart wearable devices).

Object Detection

Generative Domain-Migration Hashing for Sketch-to-Image Retrieval

1 code implementation ECCV 2018 Jingyi Zhang, Fumin Shen, Li Liu, Fan Zhu, Mengyang Yu, Ling Shao, Heng Tao Shen, Luc van Gool

The generative model learns a mapping that the distributions of sketches can be indistinguishable from the distribution of natural images using an adversarial loss, and simultaneously learns an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability.

Multi-Task Learning Sketch-Based Image Retrieval

Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating Algorithm

1 code implementation30 Aug 2018 Fan Zhu, Lin Ma, Xin Xu, Dingfeng Guo, Xiao Cui, Qi Kong

Since manual calibration is not sustainable once entering into mass production stage for industrial purposes, we here introduce a machine-learning based auto-calibration system for autonomous driving vehicles.

Autonomous Driving online learning

Baidu Apollo EM Motion Planner

1 code implementation20 Jul 2018 Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, Qi Kong

In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.

Autonomous Driving Motion Planning

Towards Automatic Construction of Diverse, High-quality Image Dataset

no code implementations22 Aug 2017 Yazhou Yao, Jian Zhang, Fumin Shen, Li Liu, Fan Zhu, Dongxiang Zhang, Heng-Tao Shen

To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.

Image Classification Object Detection

Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval

no code implementations CVPR 2017 Jin Xie, Guoxian Dai, Fan Zhu, Yi Fang

For 3D shapes, we then compute the Wasserstein barycenters of deep features of multiple projections to form a barycentric representation.

3D Shape Classification 3D Shape Retrieval

DeepShape: Deep Learned Shape Descriptor for 3D Shape Matching and Retrieval

no code implementations CVPR 2015 Jin Xie, Yi Fang, Fan Zhu, Edward Wong

Then, by imposing the Fisher discrimination criterion on the neurons in the hidden layer, we developed a novel discriminative deep auto-encoder for shape feature learning.

3D Deep Shape Descriptor

no code implementations CVPR 2015 Yi Fang, Jin Xie, Guoxian Dai, Meng Wang, Fan Zhu, Tiantian Xu, Edward Wong

Shape descriptor is a concise yet informative representation that provides a 3D object with an identification as a member of some category.

3D Shape Classification 3D Shape Retrieval

Submodular Object Recognition

no code implementations CVPR 2014 Fan Zhu, Zhuolin Jiang, Ling Shao

We present a novel object recognition framework based on multiple figure-ground hypotheses with a large object spatial support, generated by bottom-up processes and mid-level cues in an unsupervised manner.

Object Recognition

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