Cross-domain Few-shot Segmentation (CD-FSS) aims to transfer knowledge learned from source domain to distinct target domains, segmenting unseen target classes with only a few annotated samples. Although existing methods have made significant progress, they still rely on training or fine-tuning processes, which incur high computational costs and risk overfitting.
arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).
arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.
The paper introduces UINO-FSS, a unified framework for few-shot semantic segmentation that merges knowledge from DINOv2 and SAM into a single-encoder architecture. It employs coarse-to-fine multimodal distillation, a bottleneck adapter for embedding alignment, a meta-visual prompt generator, and a mask decoder, enhanced by Mamba-based 4D correlation mining. Experiments on PASCAL-5i and COCO-20i demonstrate state‑of‑the‑art performance in 1‑shot settings, achieving 80.6% mIoU on PASCAL-5i and 64.5% on COCO-20i.
arXiv:2606. 09245v1 Announce Type: cross Abstract: Few-shot object detection has gained widely attention in recent years.
arXiv:2407.03463v2 Announce Type: replace-cross Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets fo...
arXiv:2511.18031v1 Announce Type: cross Abstract: Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that i...
The paper proposes treating few-shot classification as a series of multiple instance verification (MIV) tasks, enabling effective transfer across domains without fine-tuning the backbone. It introduces the MIV-head, a backbone‑agnostic, computationally efficient classification head that achieves competitive accuracy on cross‑domain few‑shot image classification using frozen, pre‑trained backbones. Experiments on an extended Meta‑Dataset benchmark show that the MIV-head outperforms state‑of‑the‑art adapter methods while incurring lower adaptation cost.
SetCon reformulates open-ended referring segmentation as set-level concept prediction, using LVLM-generated natural-language concepts instead of segmentation-specific tokens. It employs a hierarchical semantic decomposition that first predicts a shared set-level concept and then refines it into fine-grained concept groups aligned with target subsets. The method, supported by a two-stage annotation pipeline adding hierarchical semantic supervision, achieves state-of-the-art results on multiple image and video benchmarks, with performance gains increasing as the number of referred targets grows.
arXiv:2508. 16159v2 Announce Type: replace-cross Abstract: Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extract useful inductive biases through identical network architectures.
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:2510. 22665v4 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) is a critical imaging modality due to its all-weather operational capability.