37 citations · 98 across the 11 of their papers we have counts for
13 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…
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…
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…
GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
Arthi Padmanabhan, Neil Agarwal, Anand Iyer +5
Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth overheads and privacy violations, but in doing so, face an ever-growing resource tension. Mo…
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers
Romil Bhardwaj, Zhengxu Xia, Ganesh Ananthanarayanan +6
Video analytics applications use edge compute servers for the analytics of the videos (for bandwidth and privacy). Compressed models that are deployed on the edge servers for infer…
Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination
Fuxun Yu, Zirui Xu, Tong Shen +12
Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. A…
HyperSTAR: Task-Aware Hyperparameters for Deep Networks
Gaurav Mittal, Chang Liu, Nikolaos Karianakis +3
While deep neural networks excel in solving visual recognition tasks, they require significant effort to find hyperparameters that make them work optimally. Hyperparameter Optimiza…
Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning
Fuxun Yu, Di Wang, Yinpeng Chen +7
Current state-of-the-art object detectors can have significant performance drop when deployed in the wild due to domain gaps with training data. Unsupervised Domain Adaptation (UDA…
Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-Identification
Nikolaos Karianakis, Zicheng Liu, Yinpeng Chen +1
We address the problem of person re-identification from commodity depth sensors. One challenge for depth-based recognition is data scarcity. Our first contribution addresses this p…
An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability
Nikolaos Karianakis, Jingming Dong, Stefano Soatto
We conduct an empirical study to test the ability of Convolutional Neural Networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, sca…
Boosting Convolutional Features for Robust Object Proposals
Nikolaos Karianakis, Thomas J. Fuchs, Stefano Soatto
Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many c…
Visual Scene Representations: Contrast, Scaling and Occlusion
Stefano Soatto, Jingming Dong, Nikolaos Karianakis
We study the structure of representations, defined as approximations of minimal sufficient statistics that are maximal invariants to nuisance factors, for visual data subject to sc…