The field of computer vision is moving towards more efficient architectures that can handle complex tasks such as image recognition, object detection, and lip reading. Recent developments have focused on designing lightweight models that can achieve state-of-the-art performance while reducing computational costs. Notably, the integration of transformer architectures and wavelet-based spectral decomposition has shown promising results in improving spatial-frequency modeling and mitigating computational bottlenecks. Additionally, the use of dynamic pooling strategies and efficient sub-pixel convolutional neural networks has enabled efficient super-resolution and distress detection in infrastructure images. Overall, the field is shifting towards more innovative and efficient solutions that can balance parameter efficiency and multi-scale representation. Some noteworthy papers include:

• LRTI-VSR, which proposes a novel training framework for recurrent video super-resolution that efficiently leverages long-range refocused temporal information.
• DPNet, which introduces a dynamic pooling network for tiny object detection that achieves input-aware downsampling and saves computational resources.
• Hyb-KAN ViT, which integrates wavelet-based spectral decomposition and spline-optimized activation functions to enhance spatial-frequency modeling in vision transformers.