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Found 5 papers, 1 papers with code
Segment Any 4D Gaussians
However, there is a dearth of research focusing on segmentation within 4D representations.
Segment Any 3D Gaussians
This is achieved by attaching an scale-gated affinity feature to each 3D Gaussian to endow it a new property towards multi-granularity segmentation.
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
