9 citations · 13 across the 7 of their papers we have counts for
8 papers
DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos
Bomiao Wang, Zekai Shao, Jiexiang Lan +3
While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leaving a critical gap in understa…
ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation
Zhenghan Chen, Zekai Shao, Lidan Tan +10
Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large language models (MLLMs) offer ne…
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning
Zhicong Li, Lingjie Jiang, Yulan Hu +7
Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with natural-language feedback. However,…
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings
Yilin Ye, Junchao Huang, Xingchen Zeng +2
Cross-modal embeddings form the foundation for multi-modal models. However, visualization methods for interpreting cross-modal embeddings have been primarily confined to traditiona…
GenColor: Generative Color-Concept Association in Visual Design
Yihan Hou, Xingchen Zeng, Yusong Wang +3
Existing approaches for color-concept association typically rely on query-based image referencing, and color extraction from image references. However, these approaches are effecti…
Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning
Xingchen Zeng, Haichuan Lin, Yilin Ye +1
Emerging multimodal large language models (MLLMs) exhibit great potential for chart question answering (CQA). Recent efforts primarily focus on scaling up training datasets (i.e.,…
ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map
Yilin Ye, Shishi Xiao, Xingchen Zeng +1
Multi-modal embeddings form the foundation for vision-language models, such as CLIP embeddings, the most widely used text-image embeddings. However, these embeddings are vulnerable…
IntentTuner: An Interactive Framework for Integrating Human Intents in Fine-tuning Text-to-Image Generative Models
Xingchen Zeng, Ziyao Gao, Yilin Ye +1
Fine-tuning facilitates the adaptation of text-to-image generative models to novel concepts (e.g., styles and portraits), empowering users to forge creatively customized content. R…