Summary of Sum: Saliency Unification Through Mamba For Visual Attention Modeling, by Alireza Hosseini et al.
SUM: Saliency Unification through Mamba for Visual Attention Modeling
by Alireza Hosseini, Amirhossein Kazerouni, Saeed Akhavan, Michael Brudno, Babak Taati
First submitted to arxiv on: 25 Jun 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 proposes Saliency Unification through Mamba (SUM), a novel approach that integrates the efficient long-range dependency modeling of Mamba with U-Net to provide a unified model for diverse image types. Traditional saliency prediction models, especially those based on Convolutional Neural Networks (CNNs) or Transformers, achieve notable success by leveraging large-scale annotated datasets. However, these models can be computationally expensive and often require separate models for each image type, lacking a unified approach. SUM addresses this issue by using a novel Conditional Visual State Space (C-VSS) block that dynamically adapts to various image types, including natural scenes, web pages, and commercial imagery, ensuring universal applicability across different data types. The proposed model is evaluated across five benchmarks, demonstrating consistent outperformance of existing models. This paper contributes to advancing visual attention modeling by offering a robust solution universally applicable across different types of visual content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SUM is a new approach for visual attention modeling that can be used in applications like marketing and robotics. Traditional methods are good at predicting where people will look, but they often require lots of training data and don’t work well with different types of images. SUM uses a combination of Mamba and U-Net to create a single model that works with many different image types. It also has a special block called C-VSS that helps it adapt to new images. The authors tested SUM on five different datasets and found that it worked better than other methods. This is important because visual attention modeling can be used in many real-world applications, such as trying to get people’s attention with ads or figuring out what robots should look at. |
Keywords
» Artificial intelligence » Attention
