Summary of Mementos: a Comprehensive Benchmark For Multimodal Large Language Model Reasoning Over Image Sequences, by Xiyao Wang et al.
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
by Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Mementos, a new benchmark to evaluate the sequential image reasoning abilities of Multimodal Large Language Models (MLLMs). Current benchmarks focus on static images, whereas MLLMs must be able to reason about dynamic information from image sequences. The authors test nine recent MLLMs, including GPT-4V and Gemini, on Mementos and find that they struggle to accurately describe the behavior of objects in given image sequences, often resulting in hallucinations or misrepresentations. The study identifies three key factors impacting MLLMs’ sequential image reasoning: correlation between object and behavioral hallucinations, influence of cooccurring behaviors, and compounding impact of behavioral hallucinations. This research highlights the importance of developing more advanced models that can effectively reason about dynamic visual information. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to describe what’s happening in a series of images, like a video clip. This is hard for computers to do, especially if they’re only looking at one image at a time. The authors of this paper created a new test to see how well these computer models can understand and describe moving images. They found that the models struggle to get it right, often making mistakes or coming up with things that aren’t really there. By studying what makes it hard for these models to understand moving images, we can work towards creating better ones that are more accurate. |
Keywords
* Artificial intelligence * Gemini * Gpt