From the 1 of 5 linked papers with an AI index.
5 papers
MemeBench: What LVLMs Miss When Interpreting Culture-Dependent Memes
Weihang Wang, Kainan Tu, Jielei Zhang +9
The paper presents MemeBench, a diagnostic benchmark of 1,253 Chinese and English memes that evaluates how large vision‑language models handle cultural and background knowledge, an…
TextFlux: An OCR-Free DiT Model for High-Fidelity Multilingual Scene Text Synthesis
Yu Xie, Jielei Zhang, Pengyu Chen +5
Diffusion-based scene text synthesis has progressed rapidly, yet existing methods commonly rely on additional visual conditioning modules and require large-scale annotated data to…
Efficient Causal Structure Learning via Modular Subgraph Integration
Haixiang Sun, Pengchao Tian, Zihan Zhou +3
Learning causal structures from observational data remains a fundamental yet computationally intensive task, particularly in high-dimensional settings where existing methods face c…
Improving VQA Reliability: A Dual-Assessment Approach with Self-Reflection and Cross-Model Verification
Xixian Wu, Yang Ou, Pengchao Tian +4
Vision-language models (VLMs) have demonstrated significant potential in Visual Question Answering (VQA). However, the susceptibility of VLMs to hallucinations can lead to overconf…
Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
Weihang Wang, Xinhao Li, Ziyue Wang +5
Object hallucination in Large Vision-Language Models (LVLMs) significantly impedes their real-world applicability. As the primary component for accurately interpreting visual infor…