4 citations · 6 across the 11 of their papers we have counts for
12 papers
PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation
Zhen Wang, Changpeng Wang, Zhe Liu +5
Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual observations and language instruc…
Groundbench: Multi-Resolution Polygon Grounding Exposes the Geometry Gap in Vision-Language Models
Zhonghan Bian, Zhenran Wang, Jinsong Li +1
Bounding-box scores on RefCOCO-family grounding leave little room to distinguish frontier vision-language systems, yet boxes discard object shape. We introduce GroundingBench, a ma…
SocietyBench: Forecasting Counterfactual Social-World Evolution
Zhenran Wang, Zhonghan Bian, Jinsong Li +1
Large language models (LLMs), and the agents built on top of them, are now benchmarked heavily on whether they can finish a task -- fix a bug, drive a browser, operate a GUI. A com…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
WorldCup Arena: Prospective, Leakage-Free Evaluation of Frontier LLMs on a Live Tournament
Zhenran Wang, Zhonghan Bian, Jinsong Li +1
Benchmarks that measure the forecasting ability of large language models are almost always retrospective: the event has happened, the answer is somewhere on the Web, and the evalua…
PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models
Zichao Lin, Yifeng Xie, Bowen Qu +30
We introduce PerceptionBench, a benchmark specifically designed to evaluate the atomic visual perception capabilities of Multimodal Large Language Models (MLLMs). Existing benchmar…
VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning
Zhangyang Qi, Zhixiong Zhang, Yizhou Yu +2
Vision-Language Navigation (VLN) is a core challenge in embodied AI, requiring agents to navigate real-world environments using natural language instructions. Current language mode…
GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models
Zhangyang Qi, Zhixiong Zhang, Ye Fang +2
In recent years, 2D Vision-Language Models (VLMs) have made significant strides in image-text understanding tasks. However, their performance in 3D spatial comprehension, which is…
Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images
Zhangyang Qi, Yunhan Yang, Mengchen Zhang +7
Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, deta…
Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases
Zhangyang Qi, Ye Fang, Mengchen Zhang +6
The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper…
GPT4Point: A Unified Framework for Point-Language Understanding and Generation
Zhangyang Qi, Ye Fang, Zeyi Sun +5
Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting p…
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection
Zhangyang Qi, Jiaqi Wang, Xiaoyang Wu +1
Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query…