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20232026
most citedWhere Would I Go Next? Large Language Models as Human Mobility Predictors

17 citations · 24 across the 19 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

XLocalizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization

Zichao Zeng, Weijia Fan, Yufan Chen +7

Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length…

cs.CV2026

Faster or Stronger: Towards Flexible Visual Place Recognition via Weighted Aggregation and Token Pruning

Zichao Zeng, June Moh Goo, Junwei Zheng +4

Visual Place Recognition (VPR) aims to match a query image to reference images of the same place in a large-scale database. Recent state-of-the-art methods employ Vision Transforme…

cs.CV2026

More than the Sum: Panorama-Language Models for Adverse Omni-Scenes

Weijia Fan, Ruiping Liu, Jiale Wei +7

Existing vision-language models (VLMs) are tailored for pinhole imagery, stitching multiple narrow field-of-view inputs to piece together a complete omni-scene understanding. Yet,…

cs.CV2026

RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization

Junwei Zheng, Ruize Dai, Ruiping Liu +7

Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole a…

cs.CV2026

Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation

Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9

High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise…

cs.CV2026

A Contrastive Learning Framework Empowered by Attention-based Feature Adaptation for Street-View Image Classification

Qi You, Yitai Cheng, Zichao Zeng +1

Street-view image attribute classification is a vital downstream task of image classification, enabling applications such as autonomous driving, urban analytics, and high-definitio…

cs.CV2026

A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving

June Moh Goo, Zichao Zeng, Jan Boehm

LiDAR sensors are often considered essential for autonomous driving, but high-resolution sensors remain expensive while affordable low-resolution sensors produce sparse point cloud…

cs.CV2025

Real-Time LiDAR Super-Resolution via Frequency-Aware Multi-Scale Fusion

June Moh Goo, Zichao Zeng, Jan Boehm

LiDAR super-resolution addresses the challenge of achieving high-quality 3D perception from cost-effective, low-resolution sensors. While recent transformer-based approaches like T…

cs.CV2025

Exploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labels

Weitong Kong, Zichao Zeng, Di Wen +5

Accurate perception is critical for vehicle safety, with LiDAR as a key enabler in autonomous driving. To ensure robust performance across environments, sensor types, and weather c…

cs.CV2024★ 2 cited

V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?

Natchapon Jongwiriyanurak, Zichao Zeng, June Moh Goo +7

Road safety assessments are critical yet costly, especially in Low- and Middle-Income Countries (LMICs), where most roads remain unrated. Traditional methods require expert annotat…

cs.CV2024★ 4 cited

Zero-shot detection of buildings in mobile LiDAR using Language Vision Model

June Moh Goo, Zichao Zeng, Jan Boehm

Recent advances have demonstrated that Language Vision Models (LVMs) surpass the existing State-of-the-Art (SOTA) in two-dimensional (2D) computer vision tasks, motivating attempts…

cs.CV2024★ 1 cited

Zero-shot Building Age Classification from Facade Image Using GPT-4

Zichao Zeng, June Moh Goo, Xinglei Wang +3

A building's age of construction is crucial for supporting many geospatial applications. Much current research focuses on estimating building age from facade images using deep lear…