5 papers · 1 filter
ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding
Xingyu Fu, Minqian Liu, Zhengyuan Yang +6
Structured image understanding, such as interpreting tables and charts, requires strategically refocusing across various structures and texts within an image, forming a reasoning s…
Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
Yushi Hu, Weijia Shi, Xingyu Fu +5
Humans draw to facilitate reasoning: we draw auxiliary lines when solving geometry problems; we mark and circle when reasoning on maps; we use sketches to amplify our ideas and rel…
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?
Xingyu Fu, Muyu He, Yujie Lu +2
We present a novel task and benchmark for evaluating the ability of text-to-image(T2I) generation models to produce images that align with commonsense in real life, which we call C…
BLINK: Multimodal Large Language Models Can See but Not Perceive
Xingyu Fu, Yushi Hu, Bangzheng Li +7
We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations. Most of the Blink tasks c…
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
Fei Wang, Xingyu Fu, James Y. Huang +18
We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tas…