Search Results for author: Yuheng Lu

Found 6 papers, 0 papers with code

Enhancing and Dissecting Crowd Counting By Synthetic Data

no code implementations22 Jan 2022 Yi Hou, Chengyang Li, Yuheng Lu, Liping Zhu, Yuan Li, Huizhu Jia, Xiaodong Xie

In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity.

Crowd Counting

GIR Framework: Learning Graph Positional Embeddings with Anchor Indication and Path Encoding

no code implementations29 Sep 2021 Yuheng Lu, Jinpeng Chen, Chuxiong Sun, Jie Hu

In this work, we propose a novel framework which follows the anchor-based idea and aims at conveying distance information implicitly along the MPNN message passing steps for encoding position information, node attributes, and graph structure in a more flexible way.

Graph Neural Netwrok with Interaction Pattern for Group Recommendation

no code implementations21 Sep 2021 Bojie Wang, Yuheng Lu

Specifically, our model use the graph neural network framework with powerful representation capabilities to represent the interaction between group-user-items in the topological structure of the graph, and at the same time, analyze the interaction pattern of the graph to adjust the feature output of the graph neural network, the feature representations of groups, and items are obtained to calculate the group's preference for items.

Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding

no code implementations9 May 2021 Yuheng Lu, Jinpeng Chen, Chuxiong Sun, Jie Hu

We show that GIRs get outperformed results in position-aware scenarios, and performances on typical GNNs could be improved by fusing GIR embeddings.

Representation Learning

PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition

no code implementations3 Aug 2020 Yuheng Lu, Fan Yang, Fangping Chen, Don Xie

Place recognition is one of the hot research fields in automation technology and is still an open issue, Camera and Lidar are two mainstream sensors used in this task, Camera-based methods are easily affected by illumination and season changes, LIDAR cannot get the rich data as the image could , In this paper, we propose the PIC-Net (Point cloud and Image Collaboration Network), which use attention mechanism to fuse the features of image and point cloud, and mine the complementary information between the two.

Cross-X Learning for Fine-Grained Visual Categorization

no code implementations ICCV 2019 Wei Luo, Xitong Yang, Xianjie Mo, Yuheng Lu, Larry S. Davis, Jun Li, Jian Yang, Ser-Nam Lim

Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation.

Ranked #9 on Fine-Grained Image Classification on NABirds (using extra training data)

Fine-Grained Image Classification Fine-Grained Visual Categorization

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