← Search

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 0SourcePDFScholar
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 5SourcePDFScholar
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 75PDFScholar