RGBD20K is a new large-scale RGB‑D dataset designed to advance semantic segmentation research. It contains 20,000 image pairs annotated with 160 fine‑grained categories, far exceeding the diversity of existing benchmarks such as NYUv2 and SUN RGB‑D. The authors also provide high‑fidelity annotations and introduce a score‑purified fusion (SPF) method that achieves state‑of‑the‑art results on multiple benchmarks.
The paper introduces SARTM, a framework that adapts the Segment Anything Model (SAM) for RGB‑thermal (RGB‑T) semantic segmentation. It fine‑tunes SAM with LoRA layers, incorporates language guidance, and employs a Cross‑Modal Knowledge Distillation module to bridge modality gaps. The approach also modifies the segmentation head and adds an auxiliary semantic head, achieving superior performance on MFNET, PST900, and FMB benchmarks.
By Dong Xing, Jinhe Zhang, Hang Yang, Yuqing Wang
arXiv:2605.13018v2 Announce Type: replace
Abstract: Object-centric scene understanding is a fundamental challenge in computer vision. Existing approaches often rely on multi-stage pipelines that firs...
By Yi Du, Yang You, Xiang Wan, Leonidas Guibas
arXiv:2608.22679v1 Announce Type: new
Abstract: Semantic segmentation has rapidly advanced with deep learning; however, challenges remain in effectively capturing local and global contexts as well as...
By Changki Sung, Hyungtae Lim, Wanhee Kim, Youngwoo Seo, Hyun Myung
arXiv:2605. 05627v2 Announce Type: replace-cross Abstract: Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour-intensive and geographically constrained.
By Gabriel Jeanson, David-Alexandre Duclos, William Larriv\'ee-Hardy, No\'e Cochet, Mat\v{e}j Boxan, Anthony Desch\^enes, Fran\c{c}ois Pomerleau, Philippe Gigu\`ere
arXiv:2608.21136v1 Announce Type: new
Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
By Jie Xu, Na Zhao