4 papers
CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions
Tsung-Han Wu, Heekyung Lee, Anya Ji +4
Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps. While CoT is widely effectiv…
Constantly Improving Image Models Need Constantly Improving Benchmarks
Jiaxin Ge, Grace Luo, Heekyung Lee +7
Recent advances in image generation, often driven by proprietary systems like GPT-4o Image Gen, regularly introduce new capabilities that reshape how users interact with these mode…
Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
Heekyung Lee, Jiaxin Ge, Tsung-Han Wu +3
Rebus puzzles, visual riddles that encode language through imagery, spatial arrangement, and symbolic substitution, pose a unique challenge to current vision-language models (VLMs)…
Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
Tsung-Han Wu, Heekyung Lee, Jiaxin Ge +3
Vision-Language Models (VLMs) excel at visual understanding but often suffer from visual hallucinations, where they generate descriptions of nonexistent objects, actions, or concep…