Summary of Journeybench: a Challenging One-stop Vision-language Understanding Benchmark Of Generated Images, by Zhecan Wang et al.
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images
by Zhecan Wang, Junzhang Liu, Chia-Wei Tang, Hani Alomari, Anushka Sivakumar, Rui Sun, Wenhao Li, Md. Atabuzzaman, Hammad Ayyubi, Haoxuan You, Alvi Ishmam, Kai-Wei Chang, Shih-Fu Chang, Chris Thomas
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces JourneyBench, a novel benchmark designed to evaluate multimodal large language models’ fine-grained visual understanding in various scenarios. Unlike existing benchmarks, JourneyBench focuses on imaginary contexts where language biases are insufficient, requiring models to demonstrate strong multimodal reasoning abilities. The authors release five tasks: complementary chain of thought, multi-image VQA, imaginary image captioning, VQA with hallucination triggers, and fine-grained retrieval with distractors. State-of-the-art models are benchmarked on JourneyBench, revealing that even the best-performing models struggle to reason visually in these challenging scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary JourneyBench is a new way to test how well computers understand pictures and words working together. It has five tricky tasks where computers have to use both language and vision to make good decisions. This helps us see if computers are really understanding what they’re seeing, or just using shortcuts. The best computer models didn’t do as well as we thought they would on JourneyBench, so it’s a big challenge for them! |
Keywords
» Artificial intelligence » Hallucination » Image captioning
