Search Results for author: Zhiguo Cao

Found 32 papers, 16 papers with code

Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction

no code implementations ECCV 2020 Xin Xiong, Haipeng Xiong, Ke Xian, Chen Zhao, Zhiguo Cao, Xin Li

Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.

Depth Completion graph construction

Interior Attention-Aware Network for Infrared Small Target Detection

1 code implementation IEEE Transactions on Geoscience and Remote Sensing 2022 Kewei Wang, Shuaiyuan Du, Chengxin Liu, Zhiguo Cao

Motivated by the fact that pixels from targets or backgrounds are correlated to each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection.

2D object detection 2D Semantic Segmentation

Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting

no code implementations16 Mar 2022 Min Shi, Hao Lu, Chen Feng, Chengxin Liu, Zhiguo Cao

In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric.

Composing Photos Like a Photographer

1 code implementation CVPR 2021 Chaoyi Hong, Shuaiyuan Du, Ke Xian, Hao Lu, Zhiguo Cao, Weicai Zhong

To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules.

Image Cropping

TransView: Inside, Outside, and Across the Cropping View Boundaries

no code implementations ICCV 2021 Zhiyu Pan, Zhiguo Cao, Kewei Wang, Hao Lu, Weicai Zhong

We show that relation modeling between visual elements matters in cropping view recommendation.

On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration

no code implementations10 Nov 2020 Jiaqi Yang, Zhiqiang Huang, Siwen Quan, Qian Zhang, Yanning Zhang, Zhiguo Cao

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration.

Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

1 code implementation ECCV 2020 Liang Liu, Hao Lu, Hongwei Zou, Haipeng Xiong, Zhiguo Cao, Chunhua Shen

Inspired by scale weighing, we propose a novel 'counting scale' termed LibraNet where the count value is analogized by weight.

Crowd Counting reinforcement-learning

ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification

1 code implementation11 Jul 2020 Fu Xiong, Yang Xiao, Zhiguo Cao, Yancheng Wang, Joey Tianyi Zhou, Jianxi Wu

Embedding RMML into the proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the one-pass learning manner.

Face Verification Fine-Grained Visual Recognition +1

LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching

no code implementations22 Jan 2020 Angfan Zhu, Jiaqi Yang, Weiyue Zhao, Zhiguo Cao

The local reference frame (LRF) acts as a critical role in 3D local shape description and matching.

Frame Pose Estimation

From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

3 code implementations7 Jan 2020 Haipeng Xiong, Hao Lu, Chengxin Liu, Liang Liu, Chunhua Shen, Zhiguo Cao

Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i. e., the number of population can vary in [0, inf) in theory.

Object Counting

Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology

1 code implementation1 Nov 2019 Chen Zhao, Jiaqi Yang, Xin Xiong, Angfan Zhu, Zhiguo Cao, Xin Li

To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis.

General Classification Point Cloud Classification

Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence

no code implementations3 Sep 2019 Chen Zhao, Jiaqi Yang, Ke Xian, Zhiguo Cao, Xin Li

Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.

3D Reconstruction Object Recognition

A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image

2 code implementations ICCV 2019 Fu Xiong, Boshen Zhang, Yang Xiao, Zhiguo Cao, Taidong Yu, Joey Tianyi Zhou, Junsong Yuan

For 3D hand and body pose estimation task in depth image, a novel anchor-based approach termed Anchor-to-Joint regression network (A2J) with the end-to-end learning ability is proposed.

 Ranked #1 on Depth Estimation on NYU-Depth V2 (mAP metric)

3D Pose Estimation Depth Estimation +1

Comparative evaluation of 2D feature correspondence selection algorithms

1 code implementation30 Apr 2019 Chen Zhao, Jiaqi Yang, Yang Xiao, Zhiguo Cao

Correspondence selection aiming at seeking correct feature correspondences from raw feature matches is pivotal for a number of feature-matching-based tasks.

Learning to Fuse Local Geometric Features for 3D Rigid Data Matching

no code implementations27 Apr 2019 Jiaqi Yang, Chen Zhao, Ke Xian, Angfan Zhu, Zhiguo Cao

This paper presents a simple yet very effective data-driven approach to fuse both low-level and high-level local geometric features for 3D rigid data matching.

NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences

1 code implementation CVPR 2019 Chen Zhao, Zhiguo Cao, Chi Li, Xin Li, Jiaqi Yang

Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision.

Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices

no code implementations21 Feb 2019 Guilei Hu, Yang Xiao, Zhiguo Cao, Lubin Meng, Zhiwen Fang, Joey Tianyi Zhou, Junsong Yuan

Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc.

Deception Detection Face Anti-Spoofing

Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification

1 code implementation29 Jul 2018 Fu Xiong, Yang Xiao, Zhiguo Cao, Kaicheng Gong, Zhiwen Fang, Joey Tianyi Zhou

Person re-identification is indeed a challenging visual recognition task due to the critical issues of human pose variation, human body occlusion, camera view variation, etc.

Person Re-Identification

Deep attention-based classification network for robust depth prediction

1 code implementation11 Jul 2018 Ruibo Li, Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Lingxiao Hang

However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?

Classification Deep Attention +4

Monocular Depth Estimation with Augmented Ordinal Depth Relationships

no code implementations2 Jun 2018 Yuanzhouhan Cao, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao, Shugong Xu

In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.

Monocular Depth Estimation Stereo Matching +1

Performance Evaluation of 3D Correspondence Grouping Algorithms

no code implementations6 Apr 2018 Jiaqi Yang, Ke Xian, Yang Xiao, Zhiguo Cao

This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences.

3D Object Recognition Point Cloud Registration

When Unsupervised Domain Adaptation Meets Tensor Representations

1 code implementation ICCV 2017 Hao Lu, Lei Zhang, Zhiguo Cao, Wei Wei, Ke Xian, Chunhua Shen, Anton Van Den Hengel

Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another.

Unsupervised Domain Adaptation

TasselNet: Counting maize tassels in the wild via local counts regression network

no code implementations7 Jul 2017 Hao Lu, Zhiguo Cao, Yang Xiao, Bohan Zhuang, Chunhua Shen

To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.

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