19 citations · 22 across the 16 of their papers we have counts for
16 papers · 1 filter
MaLiang-Harness: A Programmable Path to Image and Video Generation
Haoyu Zhao, Zihao Zhang, Xudong Wang +4
Executable programs offer explicit control over how images and videos are constructed, but generating runnable code is only the beginning of visual creation. A program can execute…
MobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control
Ting Huang, Yue Huang, Zeyu Zhang +2
Grounding natural-language instructions into reliable and executable actions remains a fundamental challenge for vision-language-action (VLA) systems on mobile robots, due to the p…
Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking
Shengqiong Wu, Bobo Li, Xinkai Wang +6
Unified Vision-Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework. However, existing approaches la…
Modeling Cross-vision Synergy for Unified Large Vision Model
Shengqiong Wu, Lanhu Wu, Mingyang Bao +5
Recent advances in large vision models (LVMs) have shifted from modality-specific designs toward unified architectures that jointly process images, videos, and 3D data. However, ex…
Global Commander and Local Operative: A Dual-Agent Framework for Scene Navigation
Kaiming Jin, Yuefan Wu, Shengqiong Wu +3
Vision-and-Language Scene navigation is a fundamental capability for embodied human-AI collaboration, requiring agents to follow natural language instructions to execute coherent a…
JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
Kai Liu, Jungang Li, Yuchong Sun +13
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for joint audio-video (JAV) comprehension and generation. JavisGPT has a concise encoder-LLM-…
On Path to Multimodal Generalist: General-Level and General-Bench
Hao Fei, Yuan Zhou, Juncheng Li +29
The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…
Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
Yaoting Wang, Shengqiong Wu, Yuecheng Zhang +4
By extending the advantage of chain-of-thought (CoT) reasoning in human-like step-by-step processes to multimodal contexts, multimodal CoT (MCoT) reasoning has recently garnered si…
Seeing World Dynamics in a Nutshell
Qiuhong Shen, Xuanyu Yi, Mingbao Lin +3
We consider the problem of efficiently representing casually captured monocular videos in a spatially- and temporally-coherent manner. While existing approaches predominantly rely…
Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation
Shengqiong Wu, Weicai Ye, Jiahao Wang +8
To address the bottleneck of accurate user intent interpretation within the current video generation community, we present Any2Caption, a novel framework for controllable video gen…
Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning
Shengqiong Wu, Hao Fei, Liangming Pan +3
Recent advancements in multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing various vision-language tasks. However, MLLMs face significant ch…
Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
Hao Fei, Shengqiong Wu, Hanwang Zhang +2
Recent developments of vision large language models (LLMs) have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained inst…
HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing
Jinbin Bai, Wei Chow, Ling Yang +4
We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and diverse image manipulations through op…
Towards Semantic Equivalence of Tokenization in Multimodal LLM
Shengqiong Wu, Hao Fei, Xiangtai Li +4
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization, which…
Non-confusing Generation of Customized Concepts in Diffusion Models
Wang Lin, Jingyuan Chen, Jiaxin Shi +8
We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs). It becomes even more pronounced in…
Auto-Encoding Morph-Tokens for Multimodal LLM
Kaihang Pan, Siliang Tang, Juncheng Li +6
For multimodal LLMs, the synergy of visual comprehension (textual output) and generation (visual output) presents an ongoing challenge. This is due to a conflicting objective: for…