Visual recognition tasks are often regarded as challenging mainly due to the inherent complexity, variability, and ambiguity in real-world visual data. Advanced algorithms are usually needed to address these inherent difficulties. Recent progress in deep learning techniques have made significant progress in addressing these challenges. Particularly convolutional neural networks (CNNs) and vision transformers (ViT). However, the computational graph and the network parameters of these deep learning models are usually fixed once they are trained. Therefore, the inference is mainly performed in a static manner. Such behaviors may limit their representation ability, efficiency, and interoperability. This dissertation represents a comprehensive exploration into dynamic neural networks, focusing on enhancing both their efficiency and adaptability in processing visual information. The fundamental aspect of this research lies in integrating dynamic mechanism at various levels of neural network architecture, specifically targeting convolutional processes, spatial activation, and training methodologies. Specifically, a Dynamic Parallel Pyramid (DPP) block is first proposed, offering adaptability to multi-scale feature fusion and improving performance with negligible additional computational cost. The DPP methodology revolutionizes how networks handle multiscale information, employing a novel approach that dynamically adjusts receptive field sizes based on input data. This adaptability is not just a theoretical enhancement but is empirically validated through significant performance improvements demonstrated in various scene recognition datasets. Building upon this foundation, the research then shifts towards enhancing the use of multi-scale features more effectively. This is achieved through the introduction of Spatially Selective Activation (SSA) for visual recognition. SSA moves beyond the constraints of traditional activation methods by introducing dynamic spatial conditions that adapt to individual samples. This method marks a substantial leap in the network’s ability to process visual information, showcasing robust improvements across various recognition tasks. Finally, the research journey focuses on optimizing the training efficiency of dynamic networks. Here, the Self-Supervised Efficient Sample Weighting (SESW) method comes into play, offering a streamlined approach to enhance the accuracy and efficiency of multi-exit networks. By reimagining the training process as a self-supervised multiclass classification challenge, SESW propels dynamic networks towards greater efficiency, reducing the training time while maintaining high accuracy and inference efficiency. Overall, these contributions represent a systematic and methodical advancement in dynamic neural networks. This progression from dynamic convolutional processes to refined training methodologies exemplifies a comprehensive approach to enhancing neural network architectures, marking a significant milestone in the journey towards more advanced and dynamic visual recognition systems. Keywords: Dynamic Networks, Multi-scale Features, Visual Recognition, Interoperability