most citedUrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces

17 citations · 17 across the 8 of their papers we have counts for

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cs.CV2025

AirCopBench: A Benchmark for Multi-drone Collaborative Embodied Perception and Reasoning

Jirong Zha, Yuxuan Fan, Tianyu Zhang +4

Multimodal Large Language Models (MLLMs) have shown promise in single-agent vision tasks, yet benchmarks for evaluating multi-agent collaborative perception remain scarce. This gap…

cs.CV2025

DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking

Jirong Zha, Yuxuan Fan, Kai Li +4

State estimation is challenging for 3D object tracking with high maneuverability, as the target's state transition function changes rapidly, irregularly, and is unknown to the esti…

cs.CV2025

A Unified and Scalable Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability

Jie Zhu, Jirong Zha, Ding Li +1

Self-supervised learning shows promise in harnessing extensive unlabeled data, but it also confronts significant privacy concerns, especially in vision. In this paper, we perform m…

cs.CV2025

How to Enable LLM with 3D Capacity? A Survey of Spatial Reasoning in LLM

Jirong Zha, Yuxuan Fan, Xiao Yang +2

3D spatial understanding is essential in real-world applications such as robotics, autonomous vehicles, virtual reality, and medical imaging. Recently, Large Language Models (LLMs)…

cs.CV2025★ 17 cited

UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces

Baining Zhao, Jianjie Fang, Zichao Dai +8

Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban 3D space remain to be explored. We introduce a ben…

cs.CV2024

A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability

Jie Zhu, Jirong Zha, Ding Li +1

Self-supervised learning shows promise in harnessing extensive unlabeled data, but it also confronts significant privacy concerns, especially in vision. In this paper, we aim to pe…