6 papers
MARS - A foundational map auto-regressor
Qi Zhang, Suvam Bag, Rupanjali Kukal +9
Map generation tasks feature extensive non-structural vectorized data (e.g., points, polylines, and polygons) and thus pose significant challenges to common pixel-wise generative m…
Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
Jiancheng Zhang, Meiqing Li, Qi Zhang +1
Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly…
Unlocking Zero-Shot Geospatial Reasoning via Indirect Rewards
Chenhui Xu, Fuxun Yu, Michael J. Bianco +15
Training robust reasoning vision-language models (VLMs) in rare domains (such as geospatial) is fundamentally constrained by supervision scarcity. While raw geospatial imagery is a…
Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)
Zirui Xu, Raphael Tang, Mike Bianco +4
EarthVision Embed2Scale challenge (CVPR 2025) aims to develop foundational geospatial models to embed SSL4EO-S12 hyperspectral geospatial data cubes into embedding vectors that fac…
No Forgetting Learning: Buffer-free Continual Learning Classification
Mohammad Ali Vahedifar, Qi Zhang
Most Continual Learning (CL) methods maintain performance on earlier tasks by storing exemplars in a replay buffer, introducing memory overhead that scales with the number of tasks…
Online Learning via Memory: Retrieval-Augmented Detector Adaptation
Yanan Jian, Fuxun Yu, Qi Zhang +3
This paper presents a novel way of online adapting any off-the-shelf object detection model to a novel domain without retraining the detector model. Inspired by how humans quickly…