activity
20242026
most citedScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents

1 citations · 1 across the 16 of their papers we have counts for

collaborators
Showing cs.CVShow all

12 papers · 1 filter

cs.CV2026

GIDE: Unlocking Diffusion LLMs for Precise Training-Free Image Editing

Zifeng Zhu, Jiaming Han, Jiaxiang Zhao +2

While Diffusion Large Language Models (DLLMs) have demonstrated remarkable capabilities in multi-modal generation, performing precise, training-free image editing remains an open c…

cs.CV2026

Cubic Discrete Diffusion: Discrete Visual Generation on High-Dimensional Representation Tokens

Yuqing Wang, Chuofan Ma, Zhijie Lin +7

Visual generation with discrete tokens has gained significant attention as it enables a unified token prediction paradigm shared with language models, promising seamless multimodal…

cs.CV2026

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

Yuang Ai, Jiaming Han, Shaobin Zhuang +8

We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets…

cs.CV2026

UniWeTok: An Unified Binary Tokenizer with Codebook Size for Unified Multimodal Large Language Model

Shaobin Zhuang, Yuang Ai, Jiaming Han +12

Unified Multimodal Large Language Models (MLLMs) require a visual representation that simultaneously supports high-fidelity reconstruction, complex semantic extraction, and generat…

cs.CV2025

ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection

Ruize Ma, Minghong Cai, Yilei Jiang +7

Recent progress in video generative models has enabled the creation of high-quality videos from multimodal prompts that combine text and images. While these systems offer enhanced…

cs.CV2025

Growing Visual Generative Capacity for Pre-Trained MLLMs

Hanyu Wang, Jiaming Han, Ziyan Yang +6

Multimodal large language models (MLLMs) extend the success of language models to visual understanding, and recent efforts have sought to build unified MLLMs that support both unde…

cs.CV2025★ 1 cited

ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents

Yilei Jiang, Yaozhi Zheng, Yuxuan Wan +4

Automating the transformation of user interface (UI) designs into front-end code holds significant promise for accelerating software development and democratizing design workflows.…

cs.CV2025

Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations

Jiaming Han, Hao Chen, Yang Zhao +6

This paper presents a multimodal framework that attempts to unify visual understanding and generation within a shared discrete semantic representation. At its core is the Text-Alig…

cs.CV2025

CrossLMM: Decoupling Long Video Sequences from LMMs via Dual Cross-Attention Mechanisms

Shilin Yan, Jiaming Han, Joey Tsai +5

The advent of Large Multimodal Models (LMMs) has significantly enhanced Large Language Models (LLMs) to process and interpret diverse data modalities (e.g., image and video). Howev…

cs.CV2025

Multimodal Long Video Modeling Based on Temporal Dynamic Context

Haoran Hao, Jiaming Han, Yiyuan Zhang +1

Recent advances in Large Language Models (LLMs) have led to significant breakthroughs in video understanding. However, existing models still struggle with long video processing due…

cs.CV2024

AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?

Kaixiong Gong, Kaituo Feng, Bohao Li +8

Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While th…

cs.CV2024

RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models

Haoran Hao, Jiaming Han, Changsheng Li +2

The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowled…