Summary of Recurrent Neural Networks For Still Images, by Dmitri (dima) Lvov et al.
Recurrent Neural Networks for Still Images
by Dmitri, Lvov, Yair Smadar, Ran Bezen
First submitted to arxiv on: 10 Sep 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 A novel application of Recurrent Neural Networks (RNNs) is explored in this paper for processing still images, challenging the traditional dominance of Convolutional Neural Networks (CNNs) and transformers. By treating pixel values as a sequence, RNNs can effectively handle single images, offering advantages for compact models designed for embedded systems with limited resources. A custom BiDirectional RNN (BiRNN) is introduced, which is more memory-efficient than traditional implementations. Experimental results on COCO and CIFAR100 datasets demonstrate the superiority of Convolutional Recurrent Neural Networks (CRNNs), comprising Conv2D layers and RNN layers at or near the end, particularly for small networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that Recurrent Neural Networks (RNNs) can be used to analyze still images. Usually, computers use Convolutional Neural Networks (CNNs) or transformers for this job. But RNNs are better suited for processing sequences of information over time, not just single pictures. The researchers found a way to make RNNs work with still images by treating each pixel as part of a sequence. This is useful for building small models that can fit on devices with limited power. The team also created a new type of RNN called BiDirectional RNN (BiRNN) that uses memory more efficiently. By combining these RNN layers with Conv2D layers in Convolutional Recurrent Neural Networks (CRNNs), they got better results than usual. |
Keywords
» Artificial intelligence » Rnn
