The paper introduces AquaOV255, a large-scale underwater segmentation dataset with 255 categories and over 20,000 images, and establishes the UOVSBench benchmark by combining it with five other datasets. It also presents Earth2Ocean, a training‑free open‑vocabulary segmentation framework that adapts terrestrial vision‑language models to underwater scenes using a Geometric‑guided Visual Mask Generator and a Category‑visual Semantic Alignment module. Experiments on UOVSBench show that Earth2Ocean significantly improves performance while keeping inference efficient.
By Bingyu Li, Tao Huo, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
Open-vocabulary remote sensing segmentation has recently emerged as a promising paradigm that enables pixel-level recognition of arbitrary categories specified by natural language, including classes unseen during training. However, geospatial domain shifts caused by heterogeneous regions, spatial resolutions, and acquisition platforms weaken visual-text matching and limit cross-dataset generalization.
CoralscapesV2 is an expanded dataset for coral reef visual scene understanding, increasing the number of fine‑grained classes from 39 to 95 and adding 65,000 exhaustive fish instance masks. It supports panoptic segmentation by providing high‑quality semantic and instance labels across diverse, unconstrained reef imagery. The dataset serves as a challenging benchmark for modern segmentation models and enables broader applications such as benthic cover mapping and automated fish‑reef interaction analysis.
By Jonathan Sauder, Thomas Ruckli, Gabriel\.e Strodomskyt\.e, Ibrahim Souleiman Abdallah, Rahma Hassan Abdi, Djama Goumaneh Awaleh, Mohamed Houssein Farah, Moustapha Nour, Osama Sharhubil Saad, Mustafa Mohammed Khalafallah Altaib, Maysoon Kteifan, Farah Alsoqi, Eyad Zgool, Jafar Al-Omari, Temesgen Gebremeskel Gebreluel, Zekaria Zekeria Abdulkerim, Meron Ghirmay, Teklehaimanot Beraki, Devis Tuia, Guilhem Banc-Prandi
arXiv:2508.11256v2 Announce Type: replace-cross
Abstract: Dense visual perception tasks have been constrained by their reliance on predefined categories, limiting their applicability in real-world sc...
By Junjie Wang, Keyu Chen, Yulin Li, Bin Chen, Hengshuang Zhao, Xiaojuan Qi, Zhuotao Tian
arXiv:2610.33478v1 Announce Type: cross
Abstract: Marine video understanding is crucial for a wide range of real-world applications. However, existing marine video datasets suffer from limitations, i...
By Quang-Trung Truong, Thanh-Nghia Nguyen, Phu-Phong Vo, Jaeyeon Kim, Cheng Chen, Jie Zhang, Binh-Son Hua, Duc Thanh Nguyen, Ivor Tsang, Sai-Kit Yeung
arXiv:2610.35312v1 Announce Type: new
Abstract: Monocular occupancy prediction in indoor scenes aims to recover a scene's 3D geometry and semantics from a single RGB image. Existing methods are restr...
By Shuyang Yu, Haibo Qin, Shaohan Hu, Wei Shan, Xiaoqing Guan, You Wang, Guang Li
ViCo-SAM3 introduces a Vision-Conditioned alignment framework for open-vocabulary camouflaged object segmentation. The approach adds a vision-conditioned (ViCo) module that dynamically adjusts text embeddings based on global visual context, and a vision-conditioned cross-modal binding (ViCoBind) module to improve interaction between visual and textual representations. These innovations close the semantic gap between text and pixel-level cues, enabling state‑of‑the‑art performance on the OVCamo benchmark without heavy parameter overhead.
By Qiangqiang Zhou, Wenjun Tang, Yong Chen, Dandan Zhu, Jiawei Xu
arXiv:2607. 26107v1 Announce Type: cross Abstract: Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions.
By Xinran Liu, Shouqian Shi, Yutong Chen, Ge Wang, Xin-Wei Yao, Sheng Zhong
Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes. PROB improves unknown discovery by modeling class-agnostic probabilistic objectness in the decoder-query space.
The paper introduces OVRSISBench, a unified benchmark for open‑vocabulary remote sensing image segmentation, and evaluates existing OVS/OVRSIS models, uncovering their shortcomings in remote sensing contexts. Leveraging insights from this evaluation, the authors propose RSKT‑Seg, a new framework featuring a Multi‑Directional Cost Map Aggregation module, an Efficient Cost Map Fusion transformer, and a Remote Sensing Knowledge Transfer module. Experiments on the benchmark demonstrate that RSKT‑Seg outperforms strong baselines by +3.8 mIoU and +5.9 mACC while achieving twice the inference speed.
By Bingyu Li, Haocheng Dong, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
arXiv:2608.20720v1 Announce Type: new
Abstract: Open-world 3D affordance grounding requires localizing functional object parts in 3D given free-form language queries. Existing methods typically assum...
By Junqi Wu, Kaihua Tang, Xuanwen Chen, Hongzhi Li, Jianqiang Huang, Xian-Sheng Hua
arXiv:2610.09502v1 Announce Type: new
Abstract: Open-vocabulary detection (OVD) recognizes categories unseen during training through textual category queries, yet achieving strong generalization with...
By Yupeng Zhang, Ziyi Zhao, Juntao Cheng, Sheng Wang, Ningnan Guo, Ruize Han, Liang Wan