2 citations · 5 across the 10 of their papers we have counts for
15 papers
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding
Xuyang Liu, Shihao Wang, Xinyu Wei +5
With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly important. Existing video understa…
VIEWCRAFT: Evolving What Multimodal Agents See and How They Read It
Binxiao Xu, Ruichuan An, Bocheng Zou +7
Reusable skills improve multimodal agents without updating model weights, but their effectiveness depends on both the evidence presented and the procedure used to read it. We intro…
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
Shihao Wang, Shilong Liu, Yuanguo Kuang +10
Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are l…
Rethinking VLM Representation for VLA Initialization
Weifeng Lin, Siyuan Huang, Hao Li +5
Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is…
GENIUS: Generative Fluid Intelligence Evaluation Suite
Ruichuan An, Sihan Yang, Ziyu Guo +8
Unified Multimodal Models (UMMs) have shown remarkable progress in visual generation. Yet, existing benchmarks predominantly assess , which reli…
MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition
Xinyu Wei, Kangrui Cen, Hongyang Wei +6
In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., Multi-Image Composition (MICo), remains a challenging problem, p…
VideoVerse: Does Your T2V Generator Have World Model Capability to Synthesize Videos?
Zeqing Wang, Xinyu Wei, Bairui Li +5
The recent rapid advancement of Text-to-Video (T2V) generation technologies are engaging the trained models with more world model ability, making the existing benchmarks increasing…
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPO
Chengzhuo Tong, Ziyu Guo, Renrui Zhang +5
Recent advancements underscore the significant role of Reinforcement Learning (RL) in enhancing the Chain-of-Thought (CoT) reasoning capabilities of large language models (LLMs). T…
Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos
Weifeng Lin, Xinyu Wei, Ruichuan An +7
We present Perceive Anything Model (PAM), a conceptually straightforward and efficient framework for comprehensive region-level visual understanding in images and videos. Our appro…
TIIF-Bench: How Does Your T2I Model Follow Your Instructions?
Xinyu Wei, Jinrui Zhang, Zeqing Wang +4
The rapid advancements of Text-to-Image (T2I) models have ushered in a new phase of AI-generated content, marked by their growing ability to interpret and follow user instructions.…
PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions
Weifeng Lin, Xinyu Wei, Renrui Zhang +8
This paper presents a versatile image-to-image visual assistant, PixWizard, designed for image generation, manipulation, and translation based on free-from language instructions. T…
MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine
Renrui Zhang, Xinyu Wei, Dongzhi Jiang +11
The mathematical capabilities of Multi-modal Large Language Models (MLLMs) remain under-explored with three areas to be improved: visual encoding of math diagrams, diagram-language…
MR-MLLM: Mutual Reinforcement of Multimodal Comprehension and Vision Perception
Guanqun Wang, Xinyu Wei, Jiaming Liu +5
In recent years, multimodal large language models (MLLMs) have shown remarkable capabilities in tasks like visual question answering and common sense reasoning, while visual percep…
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
Weifeng Lin, Xinyu Wei, Ruichuan An +6
In this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). V…
Cloud-Device Collaborative Learning for Multimodal Large Language Models
Guanqun Wang, Jiaming Liu, Chenxuan Li +8
The burgeoning field of Multimodal Large Language Models (MLLMs) has exhibited remarkable performance in diverse tasks such as captioning, commonsense reasoning, and visual scene u…