37 citations · 128 across the 32 of their papers we have counts for
12 papers · 1 filter
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation
Shijie Zhou, Ruiyi Zhang, Yufan Zhou +1
Large multimodal models still struggle with text-rich images because of inadequate training data. Self-Instruct provides an annotation-free way for generating instruction data, but…
SUGAR: Subject-Driven Video Customization in a Zero-Shot Manner
Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu +3
We present SUGAR, a zero-shot method for subject-driven video customization. Given an input image, SUGAR is capable of generating videos for the subject contained in the image and…
TTVD: Towards a Geometric Framework for Test-Time Adaptation Based on Voronoi Diagram
Mingxi Lei, Chunwei Ma, Meng Ding +3
Deep learning models often struggle with generalization when deploying on real-world data, due to the common distributional shift to the training data. Test-time adaptation (TTA) i…
SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding
Jian Chen, Ruiyi Zhang, Yufan Zhou +6
Multimodal large language models (MLLMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page visually-rich documen…
TextLap: Customizing Language Models for Text-to-Layout Planning
Jian Chen, Ruiyi Zhang, Yufan Zhou +4
Automatic generation of graphical layouts is crucial for many real-world applications, including designing posters, flyers, advertisements, and graphical user interfaces. Given the…
MMR: Evaluating Reading Ability of Large Multimodal Models
Jian Chen, Ruiyi Zhang, Yufan Zhou +3
Large multimodal models (LMMs) have demonstrated impressive capabilities in understanding various types of image, including text-rich images. Most existing text-rich image benchmar…
LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models
Ruiyi Zhang, Yufan Zhou, Jian Chen +3
Large multimodal language models have demonstrated impressive capabilities in understanding and manipulating images. However, many of these models struggle with comprehending inten…
TRINS: Towards Multimodal Language Models that Can Read
Ruiyi Zhang, Yanzhe Zhang, Jian Chen +4
Large multimodal language models have shown remarkable proficiency in understanding and editing images. However, a majority of these visually-tuned models struggle to comprehend th…
ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models
Jianyi Zhang, Yufan Zhou, Jiuxiang Gu +5
Diffusion models have demonstrated exceptional capabilities in generating a broad spectrum of visual content, yet their proficiency in rendering text is still limited: they often g…
Toffee: Efficient Million-Scale Dataset Construction for Subject-Driven Text-to-Image Generation
Yufan Zhou, Ruiyi Zhang, Kaizhi Zheng +5
In subject-driven text-to-image generation, recent works have achieved superior performance by training the model on synthetic datasets containing numerous image pairs. Trained on…
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models
Zihao Lin, Mohammad Beigi, Hongxuan Li +5
Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new facts into Large Language Models (LLMs). Two mainstream ME methods exist: parameter-m…
Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints
Jian Chen, Ruiyi Zhang, Yufan Zhou +4
Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (e.g., document and web designs) with constraint…