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
arXiv:2608. 07886v1 Announce Type: cross Abstract: Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization problem: given a prespecified text phrase or category name, identify the corresponding image region.
By Jieyu Zhang, Ziqi Gao, Luke Zettlemoyer, Ranjay Krishna
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.
By Zhixiong Zhang, Yizhuo Li, Shuangrui Ding, Yuhang Zang, Shengyuan Ding, Long Xing, Yibin Wang, Qiaosheng Zhang, Jiaqi Wang
arXiv:2501.04001v4 Announce Type: replace
Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...
By Haobo Yuan, Xiangtai Li, Tao Zhang, Yueyi Sun, Zilong Huang, Shilin Xu, Shunping Ji, Yunhai Tong, Lu Qi, Jiashi Feng, Ming-Hsuan Yang
arXiv:2608. 11681v1 Announce Type: cross Abstract: This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen object categories without exhaustive human annotations.
By Duy Tran Thanh, Yeejin Lee, Byeongkeun Kang
PixVL introduces a self‑supervised post‑training framework for pixel‑level multimodal large language models, addressing the scarcity of mask‑text pairs and optimization interference between region segmentation and region understanding. It employs a unified Mask‑Text Consistency Cycle that generates and self‑verifies regional descriptions using confuser‑aware semantic verification and cross‑view verification, while a quality‑coupled bidirectional learning strategy turns segmentation and understanding into mutual generators and verifiers. Experiments show that PixVL improves performance on both region understanding and segmentation tasks.
By Yicheng Xiao, Haoxuan Ma, Caorui Li, Yucheng Wu, Weijie Wang, Haoxiao Wang, Shuang Chen, Fan Yang, Haiyun Guo, Jinqiao Wang
arXiv:2608.30621v1 Announce Type: cross
Abstract: Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (V...
By Junbeom Hong, Seonghoon Yu, Hyung Rok Jung, Sundong Kim, Jeany Son
arXiv:2607. 06097v1 Announce Type: cross Abstract: 3D dense captioning, an emerging vision-language task, aims to generate descriptive sentences for each object in the 3D scene.
By Xiaopei Wu, Chenshu Hou, Liang Peng, Dan Xu, Binbin Lin, Xiaoshui Huang, Yuenan Hou, Yu Li, Wenxiao Wang, Haifeng Liu, Deng Cai, Wanli Ouyang
arXiv:2502.04192v5 Announce Type: replace
Abstract: Multiple works have emerged to push the boundaries of multi-modal large language models (MLLMs) towards pixel-level understanding. The current tren...
By Mennatullah Siam
Improving video captioning quality typically demands retraining large vision-language models, an expensive and often impractical requirement. Existing training-free alternatives instead ground captions in detected objects to curb hallucination, but apply only a single, fixed correction pass without prioritizing which objects matter most, leaving semantically significant content omitted.
This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen object categories without exhaustive human annotations. Existing methods often suffer from noisy pseudo-masks, limited visual-textual grounding, and difficulty handling synonyms or out-of-vocabulary (OOV) words.
arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.
By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li