# DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model
## 2 Related Work  
### Research on Lightweight Models  
In the current development stage of behavior recognition tasks, researchers' attention is no longer limited to continuously improving the recognition accuracy of models. Instead, they pay more attention to lightweight models with fewer parameters, a smaller size, and shorter inference times. Such models are crucial for effective deployment in environments with limited hardware resources, especially in scenarios that require edge computing, such as mobile devices, embedded systems, and miniaturized machines. Currently, there are three main methods to obtain lightweight models: the first is to prune classic models; the second is to design lightweight models based on manual experience; and the third is to use the Neural Architecture Search (NAS) to generate efficient models. Pruning is one of the most commonly used methods to obtain lightweight models. This method reduces the size and computational complexity of classic models by pruning weights, channels, or structures while maintaining high performance. VGGNet pruning [3] achieves channel pruning by applying L1 regularization to the scaling factor of the Batch Normalization (BN) layer during the network training process, which is widely used in image classification and object detection tasks. Inception pruning [4] evaluates the importance of each convolutional filter and removes less significant filters to reduce the size of the model and accelerate inference. BERT pruning [5] significantly reduces the number of model parameters and computational costs by removing redundant attention heads and layers, which is applicable to natural language processing tasks. The design of lightweight models based on human experience is a traditional but still effective method for lightweight models. This method relies on experts' knowledge and experience to design network structures and optimize components to achieve model lightweighting. The main methods include using lightweight convolutions, designing compact network structures, using lightweight activation functions, and optimizing pooling and normalization. MobileNetV1 [6] introduces depthwise separable convolutions, decomposing standard convolutions into depthwise and pointwise convolutions, significantly reducing computational load while using the ReLU6 activation function to limit output range and avoid numerical overflow. MobileNetV2 [7] further improves this by adopting inverted residual blocks and linear bottlenecks, expanding channels through expansion layers, processing feature maps with depthwise separable and pointwise convolutions, and then compressing back to the input channel number. ShuffleNet [8] uses group convolutions and channel shuffle techniques to enhance the information exchange between feature maps while reducing the amount of calculation and combines residual connections to improve model performance. SqueezeNet [9] significantly reduces the number of parameters through the fire module (including the squeeze layer and the expand layer) and global average pooling. NAS is a model lightweighting method that automatically searches for the optimal neural network structure. Its implementation steps include setting goals, defining the search space, determining the search strategy, and evaluating performance. Google designed MobileNetV3 [10] through the NAS method based on reinforcement learning, optimizing the network structure, which is applicable to image classification, object detection, and real-time video processing tasks. EfficientNet-Lite [11], as a lightweight version of EfficientNet, further optimized the model structure through the NAS method and is widely used in image classification, object detection, and semantic segmentation tasks. BiX-Net [12] used a two-stage NAS algorithm to automatically search for an ultra-lightweight and effective bidirectional skip connection structure, suitable for medical image segmentation tasks.