26 citations · 55 across the 12 of their papers we have counts for
12 papers
Enhancing Zero-shot Counting via Language-guided Exemplar Learning
Mingjie Wang, Jun Zhou, Yong Dai +2
Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting…
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model
Wei Song, Jun Zhou, Mingjie Wang +3
This paper focuses on the recently popular task of point cloud completion guided by multimodal information. Although existing methods have achieved excellent performance by fusing…
FER-former: Multi-modal Transformer for Facial Expression Recognition
Yande Li, Mingjie Wang, Minglun Gong +2
The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in ex…
GCNet: Probing Self-Similarity Learning for Generalized Counting Network
Mingjie Wang, Yande Li, Jun Zhou +2
The class-agnostic counting (CAC) problem has caught increasing attention recently due to its wide societal applications and arduous challenges. To count objects of different categ…
MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting
Mingjie Wang, Juanxi Tian, Mingze Zhang +2
The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimi…
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity
Mingjie Wang, Jianxiong Guo, Weijia Jia
Federated Learning (FL) is a decentralized learning method used to train machine learning algorithms. In FL, a global model iteratively collects the parameters of local models with…
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP
Mingjie Wang, Jun Zhou, Hao Cai +1
Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire. When only count-level (weak) supervisory signal…
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting
Mingjie Wang, Hao Cai, Xianfeng Han +2
Crowd counting remains a challenging task because the presence of drastic scale variation, density inconsistency, and complex background can seriously degrade the counting accuracy…
Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting
Mingjie Wang, Hao Cai, Jun Zhou +1
Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images. These ch…
Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets
Mingjie Wang, Jun Zhou, Wendong Mao +1
Recently, Convolution Neural Networks (CNNs) obtained huge success in numerous vision tasks. In particular, DenseNets have demonstrated that feature reuse via dense skip connection…
Semi-dense Stereo Matching using Dual CNNs
Wendong Mao, Mingjie Wang, Jun Zhou +1
A robust solution for semi-dense stereo matching is presented. It utilizes two CNN models for computing stereo matching cost and performing confidence-based filtering, respectively…
3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art
Xian-Feng Han, Shi-Jie Sun, Xiang-Yu Song +1
The introduction of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of 3D point cloud, which attracts more attention to the…