2 citations · 3 across the 9 of their papers we have counts for
11 papers
TerraVis: Towards Evaluation of World-Grounded Visual Consistency in Text-to-Image Generation via MLLM Workflows
Shuai Fu, Jing Gu, Jian Zhou +3
Recent text-to-image models have made substantial progress in photorealism, aesthetics, and text-image alignment. Yet visually appealing images can still violate real-world plausib…
Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation
Jian Zhou, Xunyi Zhao, Gengze Zhou +4
Autonomous embodied agents must sustain a long decision-making loop of perceiving, acting, verifying, and self-correcting. In trained end-to-end policies and hand-designed workflow…
Automating the Design of Embodied Agent Architectures
Jian Zhou, Sihao Lin, Jin Li +3
Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, b…
One Agent to Guide Them All: Empowering MLLMs for Vision-and-Language Navigation via Explicit World Representation
Zerui Li, Hongpei Zheng, Fangguo Zhao +5
A navigable agent needs to understand both high-level semantic instructions and precise spatial perceptions. Building navigation agents centered on Multimodal Large Language Models…
VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents
Xunyi Zhao, Gengze Zhou, Qi Wu
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across a wide range of vision-language tasks. However, their performance as embodied agents, whic…
VLNVerse: A Benchmark for Vision-Language Navigation with Versatile, Embodied, Realistic Simulation and Evaluation
Sihao Lin, Zerui Li, Xunyi Zhao +10
Despite remarkable progress in Vision-Language Navigation (VLN), existing benchmarks remain confined to fixed, small-scale datasets with naive physical simulation. These shortcomin…
Ask Good Questions for Large Language Models
Qi Wu, Zhongqi Lu
Recent advances in large language models (LLMs) have significantly improved the performance of dialog systems, yet current approaches often fail to provide accurate guidance of top…
ContentV: Efficient Training of Video Generation Models with Limited Compute
Wenfeng Lin, Renjie Chen, Boyuan Liu +10
Recent advances in video generation demand increasingly efficient training recipes to mitigate escalating computational costs. In this report, we present ContentV, an 8B-parameter…
H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding
Qi Wu, Quanlong Zheng, Yanhao Zhang +8
With the rapid development of multimodal models, the demand for assessing video understanding capabilities has been steadily increasing. However, existing benchmarks for evaluating…
NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
Jiazhao Zhang, Kunyu Wang, Rongtao Xu +6
Vision-and-language navigation (VLN) stands as a key research problem of Embodied AI, aiming at enabling agents to navigate in unseen environments following linguistic instructions…
WebVLN: Vision-and-Language Navigation on Websites
Qi Chen, Dileepa Pitawela, Chongyang Zhao +3
Vision-and-Language Navigation (VLN) task aims to enable AI agents to accurately understand and follow natural language instructions to navigate through real-world environments, ul…