12 citations · 12 across the 2 of their papers we have counts for
5 papers
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short…
Delving into Spectral Clustering with Vision-Language Representations
Bo Peng, Yuanwei Hu, Bo Liu +3
Spectral clustering is known as a powerful technique in unsupervised data analysis. The vast majority of approaches to spectral clustering are driven by a single modality, leaving…
The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification
Dante Francisco Wasmuht, Otto Brookes, Maximillian Schall +21
Automated video analysis is critical for wildlife conservation. A foundational task in this domain is multi-animal tracking (MAT), which underpins applications such as individual r…
Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches
Ying Zang, Yuncan Gao, Jiangi Zhang +7
This work advances zero-shot interactive segmentation for remote sensing imagery through three key contributions. First, we propose a novel sketch-based prompting method, enabling…
SAM 2: Segment Anything in Images and Videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu +15
We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model an…