Summary of Heracles: a Hybrid Ssm-transformer Model For High-resolution Image and Time-series Analysis, by Badri N. Patro et al.
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis
by Badri N. Patro, Suhas Ranganath, Vinay P. Namboodiri, Vijay S. Agneeswaran
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Transformers have transformed image modeling tasks with adaptations like DeIT, Swin, SVT, Biformer, STVit, and FDVIT. However, these models face inductive bias and high quadratic complexity issues when dealing with high-resolution images. State space models (SSMs) such as Mamba, V-Mamba, ViM, and SiMBA offer an alternative for handling high-resolution images in computer vision tasks. These SSMs encounter instability at large network sizes and struggle to handle local information despite efficiently capturing global image information. To address these challenges, we introduce Heracles, a novel SSM that integrates local, global, and attention-based token interaction modules. Heracles leverages Hartley kernel-based state space models for global image information, localized convolutional networks for local details, and attention mechanisms for deeper layers. Our extensive experiments demonstrate that Heracles-C-small achieves 84.5% top-1 accuracy on the ImageNet dataset, while larger variants further improve performance. Additionally, Heracles excels in transfer learning tasks on various datasets, including CIFAR-10, CIFAR-100, Oxford Flowers, and Stanford Cars, as well as instance segmentation on MSCOCO. Moreover, Heracles achieves state-of-the-art results on seven time-series datasets, showcasing its ability to generalize across domains with spectral data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to analyze images called Heracles. Right now, computers are not good at looking at high-resolution images because they use old methods that don’t work well. The authors of this paper suggest using state space models instead. These models have some problems too – they can get stuck and struggle to find small details in the image. To fix these issues, the authors created a new model called Heracles. It combines different parts to look at both the big picture (global information) and the small details (local information). The results show that this new model is better than others for analyzing images. It’s also good at transferring its learning to other tasks and domains. |
Keywords
* Artificial intelligence * Attention * Instance segmentation * Time series * Token * Transfer learning
