activity
20242026
most citedUNIT: Unifying Image and Text Recognition in One Vision Encoder

1 citations · 1 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Illuminating Unified Multimodal Model for Free-form Interleaved Text-Image Generation

Chonghuinan Wang, Zhikai Chen, Chunwei Wang +9

The advancement of generative AI models capable of producing text and image marks a critical step forward in the realm of multimodal intelligence, particularly for tasks involving…

cs.CV2026

Towards Unified Multimodal Interleaved Generation via Group Relative Policy Optimization

Ming Nie, Chunwei Wang, Jianhua Han +2

Unified vision-language models have made significant progress in multimodal understanding and generation, yet they largely fall short in producing multimodal interleaved outputs, w…

cs.CV2026

SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM

Ming Nie, Dan Ding, Chunwei Wang +4

Large language models (LLMs) have demonstrated exceptional capabilities in text understanding, which has paved the way for their expansion into video LLMs (Vid-LLMs) to analyze vid…

cs.CV2025

KFFocus: Highlighting Keyframes for Enhanced Video Understanding

Ming Nie, Chunwei Wang, Hang Xu +1

Recently, with the emergence of large language models, multimodal LLMs have demonstrated exceptional capabilities in image and video modalities. Despite advancements in video compr…

cs.CV2025

ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement

Runhui Huang, Chunwei Wang, Junwei Yang +8

We present ILLUME+ that leverages dual visual tokenization and a diffusion decoder to improve both deep semantic understanding and high-fidelity image generation. Existing unified…

cs.CV2025

SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation

Zisheng Chen, Chunwei Wang, Runhui Huang +6

In this paper, we introduce SemHiTok, a unified image Tokenizer via Semantic-Guided Hierarchical codebook that provides consistent discrete representations for multimodal understan…

eess.IV2025

FreqPrior: Improving Video Diffusion Models with Frequency Filtering Gaussian Noise

Yunlong Yuan, Yuanfan Guo, Chunwei Wang +3

Text-driven video generation has advanced significantly due to developments in diffusion models. Beyond the training and sampling phases, recent studies have investigated noise pri…

cs.CV2025

Brick-Diffusion: Generating Long Videos with Brick-to-Wall Denoising

Yunlong Yuan, Yuanfan Guo, Chunwei Wang +2

Recent advances in diffusion models have greatly improved text-driven video generation. However, training models for long video generation demands significant computational power a…

cs.CV2024

ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance

Chunwei Wang, Guansong Lu, Junwei Yang +5

In this paper, we introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single…

cs.CV2024★ 1 cited

UNIT: Unifying Image and Text Recognition in One Vision Encoder

Yi Zhu, Yanpeng Zhou, Chunwei Wang +4

Currently, vision encoder models like Vision Transformers (ViTs) typically excel at image recognition tasks but cannot simultaneously support text recognition like human visual rec…