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...
By Weijian Jian, Xiaoyue Zhang, Bin Xiao, Chunyu Xie, Yixiao He, Yutao Liu, Dawei Leng, Yuhui Yin
arXiv:2509.22650v3 Announce Type: replace
Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...
By Anna Kukleva, Enis Simsar, Alessio Tonioni, Muhammad Ferjad Naeem, Federico Tombari, Jan Eric Lenssen, Bernt Schiele
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...
By Gabriele Trivigno, Marcos Alfaro, Claudia Cuttano, Gabriele Berton, Luis Pay\'a, Carlo Masone
Prompt-driven vision-language models (VLMs) hold immense promise for accelerating dense remote sensing (RS) annotation, but static models suffer from severe performance degradation when deployed on novel scenes, unseen categories, or visually confusing backgrounds. Moreover, existing unified paradigms primarily rely on intra-image specific prompts, lacking flexible task routing to adapt to multi-intent operational workflows.
The paper introduces DCR‑SAM, a framework that adapts the Segment Anything Model 3 (SAM3) for multi‑class semantic segmentation. By decoupling cross‑modal interactions, aligning textual concepts with visual representations, and adding a dense competition head and learnable background token, DCR‑SAM reduces inference latency, mitigates semantic misclassifications, and suppresses overlapping predictions. Experiments show 2.3%–10.7% performance gains, a 33× speedup over native SAM3, and strong robustness across benchmarks.
By Wenbin Liao, Hao Zhu, Yike Ma, Hao Jiang, Feng Dai
PANORAMA introduces a new panoptic grounded captioning framework that jointly generates detailed image captions and associates each phrase with precise pixel-level masks. The authors create PanoCaps, a human‑annotated benchmark with dense captions and near‑complete pixel coverage, and propose a phrase‑mask matching protocol with a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations, learns to select appropriate masks, and achieves state‑of‑the‑art grounding performance on PanoCaps and other pixel‑level tasks.
By Sara Pieri, Evangelos Kazakos, Shizhe Chen, Josef Sivic, Cordelia Schmid