F2DPAB-Net: Fight-Or-Free Optimized Distributed Patch-Wise Attention-Driven Deep Learning Network for Visual Classification
TL;DR
The research proposes the Fight-Or-Free Optimized Distributed Patch-Wise Attention-Driven Bidirectional Long Short-Term Memory Network (F2DPAB-Net) for visual classification, which outperforms existing methods, thus attaining a maximum of 0.981 Cohen's Kappa Score, 0.96 MCC, and 0.984 NPV.
Abstract
Continuous advancements of deep learning techniques have profoundly influenced Artificial Intelligence (AI) for visual classification through shifting the field from manual feature engineering to autonomous, hierarchical feature learning. On the contrary, the traditional mechanisms for visual classification relied on various challenges, including large data requirements, computational demands, model interpretability issues, and bias concerns, which severely limited accurate classification. Therefore, the research proposes the Fight-Or-Free Optimized Distributed Patch-Wise Attention-Driven Bidirectional Long Short-Term Memory Network (F2DPAB-Net) for visual classification. The Fight-Or-Free Optimization (F2Opt) algorithm significantly tunes the hyperparameters using stochastic behaviors, potentially improving convergence speed and providing a balance between local exploitation as well as global exploration. Integration of patch-wise triplet attention fusion mechanism offers parallel processing, making the model more efficient in learning long-range dependencies that substantially increase the significance while training. On top of that, utilization of multimodality features enables the model to process and understand different modalities that achieve a more comprehensive interpretation of information and improve generalization. Overall, the proposed F2DPAB-Net outperforms existing methods, thus attaining a maximum of 0.981 Cohen’s Kappa Score, 0.96 MCC, and 0.984 NPV using the COCO dataset, respectively.