Summary of A Deep Features-based Approach Using Modified Resnet50 and Gradient Boosting For Visual Sentiments Classification, by Muhammad Arslan et al.
A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification
by Muhammad Arslan, Muhammad Mubeen, Arslan Akram, Saadullah Farooq Abbasi, Muhammad Salman Ali, Muhammad Usman Tariq
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 research proposes a novel approach for Visual Sentiment Analysis (VSA) that leverages the fusion of deep learning and machine learning algorithms. The study addresses the limitations of previous VSA methods by developing a deep feature-based method for multiclass classification, which extracts deep features from modified ResNet50. Additionally, a gradient boosting algorithm is used to classify photos containing emotional content. The approach is thoroughly evaluated on two benchmarked datasets, CrowdFlower and GAPED, demonstrating exceptional performance when compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computers to understand the emotions in pictures. Right now, most research focuses on just analyzing text or just analyzing images, but this study combines both to get a better understanding of how people feel about what they see. The team developed a new way to analyze visual sentiment using a special type of artificial intelligence called deep learning and machine learning. They tested their approach on two big datasets and found that it worked really well compared to other methods. |
Keywords
» Artificial intelligence » Boosting » Classification » Deep learning » Machine learning
