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Clint Sebastian

3 accepted papers

2025

ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts

ICCV 2025poster

The recent advancements in large foundation models have driven the success of open-set image segmentation, a task focused on segmenting objects beyond predefined categories. Among various prompt types (such as points, boxes, texts, and visual references), visual reference segmentation stands out for…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation

CVPR 2024poster

The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (SAM) have shown superior performance in generating…

Cited by 5Source ↗PDF ↗Scholar ↗
2019

Privacy Protection in Street-View Panoramas Using Depth and Multi-View Imagery

CVPR 2019poster

The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pedestrians, vehicles) in street-view imagery. We prop…

Cited by 75PDF ↗Scholar ↗