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20232026
most citedWorldSimBench: Towards Video Generation Models as World Simulators

2 citations · 9 across the 14 of their papers we have counts for

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cs.CV2026

HomeGuard: VLM-based Embodied Safeguard for Identifying Contextual Risk in Household Task

Xiaoya Lu, Yijin Zhou, Zeren Chen +6

Vision-Language Models (VLMs) empower embodied agents to execute complex instructions, yet they remain vulnerable to contextual safety risks where benign commands become hazardous…

cs.CV2025

ProGuard: Towards Proactive Multimodal Safeguard

Shaohan Yu, Lijun Li, Chenyang Si +2

The rapid evolution of generative models has led to a continuous emergence of multimodal safety risks, exposing the limitations of existing defense methods. To address these challe…

cs.CV2025

HexPlane Representation for 3D Semantic Scene Understanding

Zeren Chen, Yuenan Hou, Yulin Chen +3

In this paper, we introduce the HexPlane representation for 3D semantic scene understanding. Specifically, we first design the View Projection Module (VPM) to project the 3D point…

cs.CV2024★ 2 cited

WorldSimBench: Towards Video Generation Models as World Simulators

Yiran Qin, Zhelun Shi, Jiwen Yu +10

Recent advancements in predictive models have demonstrated exceptional capabilities in predicting the future state of objects and scenes. However, the lack of categorization based…

cs.CV2024★ 2 cited

Assessment of Multimodal Large Language Models in Alignment with Human Values

Zhelun Shi, Zhipin Wang, Hongxing Fan +7

Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh). However, in…

cs.CV2024★ 2 cited

MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control

Enshen Zhou, Yiran Qin, Zhenfei Yin +5

It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow…

cs.CV2023★ 2 cited

Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE

Zeren Chen, Ziqin Wang, Zhen Wang +7

Recent studies have demonstrated Large Language Models (LLMs) can extend their zero-shot generalization capabilities to multimodal learning through instruction tuning. As more moda…