arXiv Computer Vision
FoundYou: A Unified Model for Personalized Segmentation and Retrieval
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FoundYou: A Unified Model for Personalized Segmentation and Retrieval
Personalized segmentation and personalized retrieval both aim to identify the same physical object across different images. While the former localizes the object within a target image, the latter retr...
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UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity
UnSAMv2 is a self‑supervised extension of the Segment Anything Model that can segment objects at any granularity without human annotations. It discovers mask‑granularity pairs and introduces a granularity control embedding for continuous scale adjustment, requiring only 6K unlabeled images and 0.02% extra parameters. The model outperforms SAM/SAM‑2 across 11+ benchmarks, improving metrics such as 1‑IoU from 58.0 to 73.1 and achieving state‑of‑the‑art performance on language‑guided segmentation.
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SAM-V: Geometry-Aware Segment Anything for Multi-View Instance Segmentation
SAM‑V is a geometry‑aware extension of the Segment Anything Model (SAM) that integrates 3D priors from a feed‑forward geometry model (VGGT) into 2D segmentation. It uses a prompt‑fusion mechanism to combine sparse SAM prompts with view‑specific camera tokens and local VGGT features, enabling a mask decoder that attends to both dense 2D and 3D cues. The resulting end‑to‑end system produces consistent multi‑view instance segmentation in a single forward pass, achieving significant gains on the IGGT 3D tracking benchmark without offline mask matching or explicit 3D reconstruction.
arXiv Computer Vision
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FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation
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arXiv Computer Vision
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WALDO: One-Shot Exemplar-Conditioned Object Detection in Cluttered Scenes
arXiv:2608.28216v1 Announce Type: new Abstract: Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is ab...
arXiv Computer Vision
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Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision
arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised...