Search Results for author:
Found 4 papers, 1 papers with code
Segment Any 3D Gaussians
Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation.
Segment Anything in 3D with Radiance Fields
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results.
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation.
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes
Anomaly segmentation is a crucial task for safety-critical applications, such as autonomous driving in urban scenes, where the goal is to detect out-of-distribution (OOD) objects with categories which are unseen during training.
Ranked #10 on
Anomaly Detection
on Fishyscapes L&F
