Hugging Face Trending Papers
FoundYou: A Unified Model for Personalized Segmentation and Retrieval
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arXiv Computer Vision
Sep 1
FoundYou: A Unified Model for Personalized Segmentation and Retrieval
arXiv:2608.29917v1 Announce Type: new Abstract: Personalized segmentation and personalized retrieval both aim to identify the same physical object across different images. While the former localizes...
arXiv AI
Oct 15
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.
arXiv Machine Learning
Sep 23
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
Sep 4
FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation
FoRIS is a training‑free in‑context segmentation framework that refines foreground masks through a coarse‑to‑fine process. It operates in three stages—Foreground Purification, Localization, and Consolidation—to suppress background noise, pinpoint target regions, and reconstruct complete foreground structures. The method achieves state‑of‑the‑art performance, improving mIoU by 4.5 and 4.8 points in 1‑shot and 5‑shot settings respectively.
arXiv Machine Learning
Jul 3
Object-centric LeJEPA
arXiv:2607. 02404v1 Announce Type: cross Abstract: Image encoders trained with LeJEPA can deliver strong features for downstream tasks, but, like other image-level self-supervised methods, typically require large training datasets.
arXiv Computer Vision
3d ago
MixSIS: Mixed-supervised instance segmentation
MixSIS introduces a mixed-supervised instance segmentation framework that permits a flexible combination of image, point, box, and mask annotations within a dataset, addressing the limitation of existing weakly-supervised methods that focus on a single annotation level. The authors present AnnsMixer, a baseline system that generates pseudo mask annotations from all annotation levels, even within a single image, and demonstrate its effectiveness on both WSIS and MixSIS tasks. Experiments and case studies show that MixSIS and AnnsMixer can reduce annotation time by up to 90% or improve segmentation results by 34% using simple annotation rules or heterogeneous data.