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Peishan Li

dblp:177/7487 · DBLP profile ↗
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5ranked-venue papers
2as first author
3since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 2 first-author · 3 since 2021Databases, data management, data science and information retrieval · 2Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Spatial and temporal data management · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Spatial and temporal data management
spatial databases
0.312018
POIsam: a System for Efficient Selection of Large-scale Geospatial Data on Maps · SIGMOD Conference 2018

Methods — techniques the papers use, named apart from their topics

prefetching · 0.7approximation algorithm · 0.7
YearPublicationVenuePosition
2025 FS-Net collaborative flattening transformation for high performance 3D-to-2D vessel segmentation in OCTA images
Jiwei Xing, Songyi Jiang, Dongbei Guo, Jinde Zhang, Peishan Li, Linyan Xue, Valery V. Tuchin, Xiufei Gu, Ronghui Li, Qingliang Zhao
Neurocomputing6
2024 Enduring Memory Self-learning Multi-level Transformer Network for Remote Sensing Image Super-Resolution
Peishan Li, Guangyi Ma
ICPR (20)1
2024 Adaptive Enhanced Reversible Flow Model for Remote Sensing Image Super Resolution
Peishan Li, Guangyi Ma, Ziwei Yuan
ICPR (31)1
2018 POIsam: a System for Efficient Selection of Large-scale Geospatial Data on Maps
abstract
In this demonstration we present POIsam, a visualization system supporting the following desirable features: representativeness, visibility constraint, zooming consistency, and panning consistency. The first two constraints aim to efficiently select a small set of representative objects from the current region of user's interest, and any two selected objects should not be too close to each other for users to distinguish in the limited space of a screen. One unique feature of POISam is that any similarity metrics can be plugged into POISam to meet the user's specific needs in different scenarios. The latter two consistencies are fundamental challenges to efficiently update the selection result w.r.t. user's zoom in, zoom out and panning operations when they interact with the map. POISam drops a common assumption from all previous work, i.e. the zoom levels and region cells are pre-defined and indexed, and objects are selected from such region cells at a particular zoom level rather than from user's current region of interest (which in most cases do not correspond to the pre-defined cells). It results in extra challenge as we need to do object selection via online computation. To our best knowledge, this is the first system that is able to meet all the four features to achieve an interactive visualization map exploration system.
Tao Guo 0002, Mingzhao Li 0001, Peishan Li, Zhifeng Bao, Gao Cong
SIGMOD Conference3
2016 Knowledge Graph Completion via Local Semantic Contexts
Xiangling Zhang, Cuilan Du, Peishan Li, Yangxi Li
DASFAA (1)3