Selective state models are what you need for animal action recognition

Recognizing animal actions provides valuable insights into animal welfare, yielding crucial information for agricultural, ethological, and neuroscientific research. While video-based action recognition models have been applied to this task, current approaches often rely on computationally intensive Transformer layers, limiting their practical application in field settings such as farms and wildlife reserves. This study introduces Mamba-MSQNet, a novel architecture family for multilabel Animal Action Recognition using Selective Space Models. By transforming the state-of-the-art MSQNet model with Mamba blocks, we achieve significant reductions in computational requirements: up to 90% fewer Floating point OPerations and 78% fewer parameters compared to MSQNet. These optimizations not only make the model more efficient but also enable it to outperform Transformer-based counterparts on the Animal Kingdom dataset, achieving a mean Average Precision of 74.6, marking an improvement over previous architectures. This combination of enhanced efficiency and improved performance represents a significant advancement in the field of animal action recognition. The dramatic reduction in computational demands, coupled with a performance boost, opens new possibilities for real-time animal behavior monitoring in resource-constrained environments. This enhanced efficiency could revolutionize how we observe and analyze animal behavior, potentially leading to breakthroughs in animal welfare assessment, behavioral studies, and conservation efforts. • We introduce Mamba-MSQNet an architecture for multilabel animal action recognition. • We achieve 74.6 mAP, reducing computation by up to 90% over previous SOTA. • We show that Mamba-MSQNet improve recognition of rare and critical animal behaviors. • We test our robustness on BaboonLand dataset achieving SOTA results.