arXiv:2607. 20326v1 Announce Type: cross Abstract: RGB-D semantic segmentation has achieved remarkable progress, yet most models assume that RGB and depth are always available.
By Xuchen Zhu, Yajuan Wei, Shuang Hao, Jiwei Jiang, Guanxiang Mao, Fang Ren
arXiv:2609. 03378v1 Announce Type: new Abstract: Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable.
By Xuehao Wang, Jiaxin Hua, Runmei Li, Zhenyu Wu, Chenglizhao Chen, Ke Gu, Aimin Hao
RGBD20K is a new large-scale RGB‑D semantic segmentation dataset featuring 20,000 image pairs and 160 fine‑grained categories, surpassing existing benchmarks like NYUv2 and SUN RGB‑D in both scale and semantic diversity. The dataset provides high‑fidelity annotations obtained through rigorous re‑evaluation and correction of prior labels, ensuring a clean ground‑truth foundation. Additionally, the authors introduce a score‑purified fusion (SPF) method that achieves state‑of‑the‑art performance across evaluated benchmarks, demonstrating the value of high‑quality multimodal information.
By Shaohua Dong, Zexuan Meng, Haiyan Sun, Bing Fan, Cuicui Zhang, Dylan Joseph, Kewei Sha, Yunhe Feng, Heng Fan
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.
arXiv:2610.09125v1 Announce Type: new
Abstract: Reference-based object compositing inserts or replaces an object using a background image, a reference image, and a 2D compositing mask. These inputs g...
By Sanghyun Jo, Chae Yeon Lim, Donghwan Lee, Sihyun Kim, Soo Ye Kim, Kyungsu Kim
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