Summary of Mosabench: Multi-object Sentiment Analysis Benchmark For Evaluating Multimodal Large Language Models Understanding Of Complex Image, by Shezheng Song et al.
MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image
by Shezheng Song, Chengxiang He, Shasha Li, Shan Zhao, Chengyu Wang, Tianwei Yan, Xiaopeng Li, Qian Wan, Jun Ma, Jie Yu, Xiaoguang Mao
First submitted to arxiv on: 25 Nov 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 presents MOSABench, a novel evaluation dataset designed specifically for multi-object sentiment analysis (MOSA), a crucial task in semantic understanding. The dataset comprises approximately 1,000 images with multiple objects, requiring multimodal large language models (MLLMs) to independently assess the sentiment of each object, mirroring real-world complexities. MOSABench introduces innovative features such as distance-based target annotation, post-processing for evaluation, and an improved scoring mechanism. Experiments reveal limitations in current MLLMs, including scattered focus and performance declines with increasing spatial distance between objects. Models like mPLUG-owl and Qwen-VL2 demonstrate effective attention to sentiment-relevant features, while others require improvement. This research highlights the need for MLLMs to enhance accuracy in complex MOSA tasks and establishes MOSABench as a foundational tool for advancing sentiment analysis capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to test how well computers can understand the emotions of different objects in pictures. Right now, computers are good at understanding simple sentences, but they struggle when it comes to complex tasks like recognizing emotions in multiple objects. To help computers get better at this task, researchers created a special dataset with 1,000 images and lots of objects that require the computer to understand each object’s emotion separately. The new way of testing is called MOSABench and it has some special features that make it more accurate than before. Some computer models are good at understanding emotions, but others need improvement. Overall, this research shows how computers can get better at recognizing emotions in complex scenes. |
Keywords
» Artificial intelligence » Attention
