@inproceedings{10.1145/3712256.3726451,
author = {Pinto, Rafael C. and Tavares, Anderson R.},
title = {Neuroevolution of Self-Attention Over Proto-Objects},
year = {2025},
isbn = {9798400714658},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3712256.3726451},
doi = {10.1145/3712256.3726451},
abstract = {Proto-objects - image regions that share common visual properties - offer a promising alternative to traditional attention mechanisms based on rectangular-shaped image patches in neural networks. Although previous work demonstrated that evolving a patch-based hard-attention module alongside a controller network could achieve state-of-the-art performance in visual reinforcement learning tasks, our approach leverages image segmentation to work with higher-level features. By operating on proto-objects rather than fixed patches, we significantly reduce the representational complexity: each image decomposes into fewer proto-objects than regular patches, and each proto-object can be efficiently encoded as a compact feature vector. This enables a substantially smaller self-attention module that processes richer semantic information. Our experiments demonstrate that this proto-object-based approach matches or exceeds the state-of-the-art performance of patch-based implementations with 62\% less parameters and 2.6 times less training time.},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {1300--1308},
numpages = {9},
keywords = {neuroevolution, representation learning},
location = {NH Malaga Hotel, Malaga, Spain},
series = {GECCO '25}
}