# Towards Feasible Capsule Network for Vision Tasks
## 2 Related Works  
**Capsule network in vision tasks**: Capsule networks have made significant strides in various vision tasks across domains. They have been successfully applied to hyperspectral image classification as spectral-spatial units [41]. In remote sensing image scene classification, CNN-CapsNet architectures have achieved enhanced results on diverse datasets [42]. For action detection in videos, VideoCapsuleNet, a 3D capsule network, has demonstrated superior performance with 3D capsule convolution [43]. In image synthesis, CapsuleGAN and CapsGAN have excelled at modeling image data distribution and capturing geometric transformations [44; 45]. Adversarial attacks on CapsHeads have been investigated, with studies such as CapsAttacks and Capsule-FORENSICS focusing on vulnerability analysis and detecting forged images and videos [17; 18]. In the medical imaging domain, CapsNets have shown promise in lung cancer screening and medical image segmentation [38; 46]. Furthermore, CapsNets have contributed to advancements in brain tumor type classification, offering improved interpretability of learned features [47]. These advancements collectively demonstrate the potential and versatility of CapsNets in computer vision applications.  
**Routing mechanism**: Studies have explored various routing mechanisms in CapsNets. Dynamic routing enables capsules to route information based on agreement scores [14], while matrix capsules with EM routing use an iterative expectation-maximization process for routing coefficients [28]. Alternative methods include inverted dot-product attention routing [48], shortcut routing for reduced complexity [21], and self-routing capsule networks with subordinate networks [16]. Generalized capsule networks train coupling coefficients for flexibility [32]. Efficient routing algorithms like self-attention routing and weighted kernel density estimation improve speed and efficiency [22; 49]. Max-min normalization allows for independent values within bounds [50], and spectral capsule networks employ singular value decomposition for routing [51]. These studies advance routing mechanisms, addressing computational efficiency, attention-based routing, and coefficient optimization.  
**Deep capsule network**: Numerous research efforts have focused on enhancing CapsNets for complex data analysis. Investigations into model modifications, dynamic routing algorithms, and the use of convolutional capsule layers (Conv-Caps Layers) have been proposed to improve performance [39; 52; 53]. Dense capsule networks (DCNets) and diverse capsule networks (DCNets++) replace standard convolutional layers with densely connected convolutions [34], and DeepCaps introduces a novel 3D convolution-based dynamic routing algorithm [52]. The diverse enhanced capsule network (DE-CapsNet) leverages the advantages of DCNet++ and employs residual convolutional layers for diverse enhanced primary capsules [35]. Neural network encapsulation approximates the routing process with master and aide branches, while CapsNets with residual connections introduce skip connections to facilitate training depth [24; 36]. These studies collectively advance CapsNets for complex data analysis, augmenting their efficacy in various applications.