Andrea Colaco — accepted AI-conference papers

2026

ESAM++: Efficient Online 3D Perception on the Edge

Qin Liu, Lavisha Aggarwal, Saptarashmi Bandyopadhyay, Vikas Bahirwani, Marc Niethammer, Ehsan Adeli +1

CVPR 2026

Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computational resources are limited and privacy is crucial. Recent state-of-the-art methods like EmbodiedSAM (ESAM) demonstrate the promise of online 3D pe

Cited by 0 Source ↗code ↗Scholar ↗

2024

Diffuse Attend and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion

Junjiao Tian, Lavisha Aggarwal, Andrea Colaco, Zsolt Kira, Mar Gonzalez-Franco

CVPR 2024poster

Producing quality segmentation masks for images is a fundamental problem in computer vision. Recent research has explored large-scale supervised training to enable zero-shot transfer segmentation on virtually any image style and unsupervised training to enable segmentation without dense annotations.…

Cited by 71 Source ↗PDF ↗code ↗Scholar ↗

2024

Geometry Fidelity for Spherical Images

Anders Christensen*, Nooshin Mojab*, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther +2

ECCV 2024poster

"Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of…

Cited by 2 Source ↗PDF ↗Scholar ↗