Bing Wang

Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

Xiao Wang, Bing Wang, Bin Yang, Cuiqun Chen, Xin Xu, Mang Ye

Person re-identification (ReID) serves as a critical component in intelligent surveillance systems, aiming to match identities across disjoint camera networks. While traditional methods primarily rely on single-modal RGB imagery, they are often constrained by environmental challe…Read more

GPOcc++: Unified Sparse Gaussian Occupancy Prediction with Visual Geometry Priors

Changqing Zhou, Yueru Luo, Yulan Guo, Bing Wang, Jie Qin, Changhao Chen

Accurate 3D scene understanding is fundamental to embodied intelligence and autonomous driving, where 3D occupancy provides a unified representation of objects, structures, and free space. However, recovering such a complete volumetric representation from visual observations rema…Read more

Deep Learning-Based Characterization of Detonation-Cell Size Distributions in Soot-Foil Records

Mingyang Bu, Robson A. Schneider, Karl P. Chatelain, Mhedine Alicherif, Yingchen Shi, Andrés Z. Mendiburu, et al.

The geometric size and regularity of detonation cells are key physical parameters for characterizing detonation waves. Traditional manual measurement of soot foils is time-consuming and subjective, while existing computer vision techniques often exhibit poor generalization on rea…Read more

Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook

Xuelin Zhu, Xiu-Shen Wei, Jiawei Ge, Shuai Xu, Bing Wang

Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-world applications, such as autonomous driving, disease diagnosis, recommendation system, and mobile ser…Read more

Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation

Hong Chen, Daqi Liu, Zehan Zhang, Haiguang Wang, Tianhao Lu, Longfei Yan, et al.

Existing world model-based planners for visual navigation typically follow a verification-centric paradigm, decoupling goal intent from trajectory synthesis. This approach suffers from candidate dependence, heavy computational overhead, and inconsistencies between sampled actions…Read more

ReWorld: Learning Better Representations for World Action Models

Tianze Xia, Lijun Zhou, Kaixin Xiong, Jingfeng Yao, Yu Zhu, Zhenxin Zhu, et al.

World Action Models (WAMs) model future environment evolution under action conditioning, offering a scalable paradigm for autonomous driving. However, existing approaches focus largely on model architecture design, and how a WAM can efficiently learn better world representations…Read more

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

Chunlan Zhao, Ying Jia, Yao Qu, Wenjuan Zheng, Shaodan Hou, Bing Wang

In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional…Read more

Leveraging machine learning to optimize hydrothermal technology for antibiotic degradation in antibiotic fermentation residues

Guomin Li, Yuxiang Zhang, Xiaomeng Lin, Bing Wang, Hong Jiang