Summary of Exploring the Effectiveness Of Object-centric Representations in Visual Question Answering: Comparative Insights with Foundation Models, by Amir Mohammad Karimi Mamaghan et al.
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
by Amir Mohammad Karimi Mamaghan, Samuele Papa, Karl Henrik Johansson, Stefan Bauer, Andrea Dittadi
First submitted to arxiv on: 22 Jul 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 paper investigates the potential of object-centric (OC) representations in visual question answering (VQA), a task requiring compositional understanding of scenes. Building upon recent advancements in foundation models, which have shown impressive capabilities across domains, this study aims to thoroughly validate OC models’ effectiveness in VQA tasks. The authors explore the benefits and trade-offs of OC models versus large pre-trained foundation models on synthetic and real-world data, identifying a promising path forward by leveraging strengths from both approaches. This research contributes to the understanding of systematic compositional generalization and facilitates reasoning in visual scenes, with potential applications in various downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can understand images and answer questions about them. It’s like trying to figure out what’s going on in a picture and answering simple questions about it. The authors want to see if using “object-centric” representations, which break down an image into individual objects, helps computers do this better. They compare these models to other powerful computer models that have been trained on lots of data. The results show that combining the two approaches can be really helpful in understanding images and answering questions about them. |
Keywords
» Artificial intelligence » Generalization » Question answering
