Author name cluster

Huiling Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2025 Conference Paper

Gradient-Guided Credit Assignment and Joint Optimization for Dependency-Aware Spatial Crowdsourcing

Yafei Li
Wei Chen
Jinxing Yan
Huiling Li
Lei Gao
Mingliang Xu

Dependency-aware spatial crowdsourcing (DASC) addresses the unique challenges posed by subtask dependencies in spatial task assignment. This paper investigates the task assignment problem in DASC and proposes a two-stage Recommend and Match Optimization (RMO) framework, leveraging multi-agent reinforcement learning for subtask recommendation and a multi-dimensional utility function for subtask matching. The RMO framework primarily addresses two key challenges: credit assignment for subtasks with interdependencies and maintaining overall coherence between subtask recommendation and matching. Specifically, we employ meta-gradients to construct auxiliary policies and establish a gradient connection between two stages, which can effectively address credit assignment and joint optimization of subtask recommendation and matching, while concurrently accelerating network training. We further establish a unified gradient descent process through gradient synchronization across recommendation networks, auxiliary policies, and the matching utility evaluation function. Experiments on two real-world datasets validate the effectiveness and feasibility of our proposed approach.

PDF Details DOI

EAAI Journal 2025 Journal Article

Person re-identification based on an improved DeepLabv3+ semantic segmentation network

Fuzhen Zhu
Donglin Li
Huiling Li
Yong Liu
Bing Zhu

Pedestrian re-identification is the process of recognizing and matching the same pedestrian in surveillance videos across different cameras or different time periods, which has been widely used in the field of intelligent security and security tracking. However, if the background is complex or persons are occluded, it often results in low accuracy. To solve these problems, we propose a person re-identification method based on improved DeepLabv3+ semantic segmentation, which mainly consists of two parts: semantic segmentation network and person re-identification network. Firstly, we propose an U-DeepLabv3+ semantic segmentation network by combing the advantages of the U-shaped Network(U-Net) and DeepLab version plus network(DeepLabv3+) to generate precise pedestrian masks, which can effectively eliminate background interference and provide high-quality inputs for subsequent person re-identification. Secondly, we propose an improved part-based convolutional baseline person re-identification network (IPCBReID), which leverages these masks to strengthen feature extraction and integrates an attention mechanism along with a "One-vs. -Rest" relational module to achieve deeper feature integration. Extensive experiments conducted on the Market-1501, Chinese University of Hong Kong (CUHK03), and Occluded-Duke datasets demonstrate the effectiveness of our approach. Compared with state-of-the-art methods, our IPCBReID achieves improvements in mAP by 0. 3 %, 0. 5 %, and 0. 3 % on above three datasets respectively, and increases Rank-1 accuracy by 1. 5 % and 1. 6 % on Market-1501 and Occluded-Duke. These results validate the effectiveness of our method in enhancing person re-identification accuracy.

Details DOI

EAAI Journal 2023 Journal Article

Unmanned aerial vehicle remote sensing image registration based on an improved oriented FAST and rotated BRIEF- random sample consensus algorithm

Fuzhen Zhu
Huiling Li
Jiacheng Li
Bing Zhu
Siwen Lei

Unmanned Aerial Vehicle (UAV) remote sensing image registration is the key step of remote sensing image stitching, image fusion and multi-frames image super-resolution. Its speed and accuracy determines the effect of remote sensing image applications, such as object detection, environment monitoring. To meet the speed and accuracy requirements of UAV remote sensing image registration, an improved Oriented FAST and Rotated BRIEF- Random sample consensus (ORB-RANSAC) algorithm is proposed. Firstly, images to be registered are divided into non-overlapping sub-images, and then a simplified image pyramid is constructed for these sub-images to get scale invariance. Secondly, the traditional FAST corner detection algorithm is improved by setting the adaptive corner detection threshold, and more feature points are detected. Meanwhile, the traditional quadtree algorithm is improved to remove redundant feature points and keep the remaining high-quality feature points. Thirdly, feature points coarse matching is done by bidirectional matching combined with cosine similarity method. Finally, the improved RANSAC algorithm is used for feature points fine matching to eliminate mismatches and calculate the transformation matrix. Experiment results show that, comparing with the traditional ORB algorithm, the number of feature points detected is significantly increased and its distribution is more uniform, and the correct matching rate increases by 58. 10% in the case of image scale changing. Comparing with the state-of-the-art UAV remote sensing image registration algorithm, the correct matching rate and mutual information of our method are increased by 0. 68% and 1. 91% respectively, matching time and root mean square error are reduced by 3. 89% and 11. 2% respectively.

Details DOI