model, and usually, they’re huge. They have millions of parameters. Our segmentation model is a fraction of this size and performs very well. In some cases, it actually outperforms the state-of-the-art deep learning segmentation models.” Diverging from traditional segmentation models, which usually have an encoder-decoder framework, MarsLS-Net uses a stack of blocks called Progressively Expanded Neuron Attention (PENAttention) blocks. “We’re using this concept of the progressive neuron expansion, where each neuron is progressively expanded using Maclaurin series expansion of a nonlinear function,” he explains. “We’re doing that to obtain richer and more relevant feature representation, which led to a very lightweight model in a different structure than state-of-the-art segmentation models.” Looking to the future, Abel highlights plans to improve the reliability of the dataset by utilizing image enhancement models to upsample lower-resolution images to match the higher ones. Additionally, he aims to make the architecture more trainable. “The way we’re using the progressive expansion neurons is actually fixing 12 DAILY WACV Saturday Poster Presentation
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