163 citations · 279 across the 17 of their papers we have counts for
17 papers
VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations
Yuexiong Ding, Xiaowei Luo
Currently, object detection applications in construction are almost based on pure 2D data (both image and annotation are 2D-based), resulting in the developed artificial intelligen…
Implicit Diffusion Models for Continuous Super-Resolution
Sicheng Gao, Xuhui Liu, Bohan Zeng +6
Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and mos…
A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
Huadeng Wang, Hao Xu, Bingbing Li +4
Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts…
A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification
Huadeng Wang, Zhipeng Liu, Rushi Lan +4
Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-co…
Binary Representation via Jointly Personalized Sparse Hashing
Xiaoqin Wang, Chen Chen, Rushi Lan +4
Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode…
Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling
Wanquan Feng, Jin Li, Hongrui Cai +2
In this paper, we propose Neural Points, a novel point cloud representation and apply it to the arbitrary-factored upsampling task. Different from traditional point cloud represent…
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition
Bin Sun, Dehui Kong, Shaofan Wang +3
Zero-shot action recognition can recognize samples of unseen classes that are unavailable in training by exploring common latent semantic representation in samples. However, most m…
ZooBuilder: 2D and 3D Pose Estimation for Quadrupeds Using Synthetic Data
Abassin Sourou Fangbemi, Yi Fei Lu, Mao Yuan Xu +3
This work introduces a novel strategy for generating synthetic training data for 2D and 3D pose estimation of animals using keyframe animations. With the objective to automate the…
Multi-column Point-CNN for Sketch Segmentation
Fei Wang, Shujin Lin, Hanhui Li +4
Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation…
Neural Task Planning with And-Or Graph Representations
Tianshui Chen, Riquan Chen, Lin Nie +3
This paper focuses on semantic task planning, i.e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer…
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Tianshui Chen, Wenxi Wu, Yuefang Gao +3
Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized acc…
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Tianshui Chen, Liang Lin, Riquan Chen +2
Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition…
Structured Inhomogeneous Density Map Learning for Crowd Counting
Hanhui Li, Xiangjian He, Hefeng Wu +4
In this paper, we aim at tackling the problem of crowd counting in extremely high-density scenes, which contain hundreds, or even thousands of people. We begin by a comprehensive a…
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
Daiguo Deng, Ruomei Wang, Hefeng Wu +3
Fabric image retrieval is beneficial to many applications including clothing searching, online shopping and cloth modeling. Learning pairwise image similarity is of great importanc…
Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
Tianshui Chen, Liang Lin, Wangmeng Zuo +2
Accelerating deep neural networks (DNNs) has been attracting increasing attention as it can benefit a wide range of applications, e.g., enabling mobile systems with limited computi…
Learning to Segment Object Candidates via Recursive Neural Networks
Tianshui Chen, Liang Lin, Xian Wu +2
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate obje…
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Tianshui Chen, Liang Lin, Lingbo Liu +2
Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful…