5 papers · 1 filter
Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions
Zhongbin Guo, Zhen Yang, Yushan Li +6
Recent advancements in Spatial Intelligence (SI) have predominantly relied on Vision-Language Models (VLMs), yet a critical question remains: does spatial understanding originate f…
LISA-3D: Lifting Language-Image Segmentation to 3D via Multi-View Consistency
Zhongbin Guo, Jiahe Liu, Wenyu Gao +4
Text-driven 3D reconstruction requires masks that understand free-form instructions and remain stable under viewpoint changes. We present LISA-3D, a two-stage framework that adapts…
Beyond Flatlands: Unlocking Spatial Intelligence by Decoupling 3D Reasoning from Numerical Regression
Zhongbin Guo, Jiahe Liu, Yushan Li +5
Existing Vision Language Models (VLMs) architecturally rooted in "flatland" perception, fundamentally struggle to comprehend real-world 3D spatial intelligence. This failure stems…
Understanding Temporal Logic Consistency in Video-Language Models through Cross-Modal Attention Discriminability
Chengzhi Li, Heyan Huang, Ping Jian +3
Large language models (LLMs) often generate self-contradictory outputs, which severely impacts their reliability and hinders their adoption in practical applications. In video-lang…
TAMMs: Change Understanding and Forecasting in Satellite Image Time Series with Temporal-Aware Multimodal Models
Zhongbin Guo, Yuhao Wang, Ping Jian +4
Temporal Change Description (TCD) and Future Satellite Image Forecasting (FSIF) are critical, yet historically disjointed tasks in Satellite Image Time Series (SITS) analysis. Both…