1 citations · 1 across the 8 of their papers we have counts for
10 papers
Image-Conditioned Instance Prompt Network for Referring Remote Sensing Image Segmentation
Biaoyu Ren, Qingsheng Wang, Cun Xu +2
Referring Remote Sensing Image Segmentation (RRSIS) is a situated, task-driven cross-modal task related to the embodied perception paradigm, requiring models to align visual-spatia…
Scaling World Model for Hierarchical Manipulation Policies
Qian Long, Yueze Wang, Jiaxi Song +13
Vision-Language-Action (VLA) models are promising for generalist robot manipulation but remain brittle in out-of-distribution (OOD) settings, especially with limited real-robot dat…
Unified Vision-Language-Action Model
Yuqi Wang, Xinghang Li, Wenxuan Wang +5
Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on t…
End-to-End Vision Tokenizer Tuning
Wenxuan Wang, Fan Zhang, Yufeng Cui +5
Existing vision tokenization isolates the optimization of vision tokenizers from downstream training, implicitly assuming the visual tokens can generalize well across various tasks…
Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target Granularities
Jing Liu, Wenxuan Wang, Yisi Zhang +5
Referring expression segmentation (RES) aims at segmenting the entities' masks that match the descriptive language expression. While traditional RES methods primarily address objec…
Image Difference Grounding with Natural Language
Wenxuan Wang, Zijia Zhao, Yisi Zhang +4
Visual grounding (VG) typically focuses on locating regions of interest within an image using natural language, and most existing VG methods are limited to single-image interpretat…
EVEv2: Improved Baselines for Encoder-Free Vision-Language Models
Haiwen Diao, Xiaotong Li, Yufeng Cui +6
Existing encoder-free vision-language models (VLMs) are rapidly narrowing the performance gap with their encoder-based counterparts, highlighting the promising potential for unifie…
Diffusion Feedback Helps CLIP See Better
Wenxuan Wang, Quan Sun, Fan Zhang +3
Contrastive Language-Image Pre-training (CLIP), which excels at abstracting open-world representations across domains and modalities, has become a foundation for a variety of visio…
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions
Wenxuan Wang, Yisi Zhang, Xingjian He +4
Visual grounding (VG) aims at locating the foreground entities that match the given natural language expressions. Previous datasets and methods for classic VG task mainly rely on t…
Unveiling Parts Beyond Objects:Towards Finer-Granularity Referring Expression Segmentation
Wenxuan Wang, Tongtian Yue, Yisi Zhang +4
Referring expression segmentation (RES) aims at segmenting the foreground masks of the entities that match the descriptive natural language expression. Previous datasets and method…