The field of computer vision is witnessing a significant shift towards adaptive and efficient models, inspired by human-like vision and cognition. Recent developments have focused on designing models that can selectively focus on relevant regions of an image, rather than processing the entire scene at once. This approach has led to improved performance, reduced computational costs, and enhanced interpretability. Notably, the integration of attention mechanisms and reinforcement learning has enabled models to learn task-relevant features and make decisions based on sequential observations. Furthermore, the incorporation of cognitive principles, such as saccadic vision and latent learning, has opened up new avenues for improving model generalization and flexibility. Overall, these advancements are poised to revolutionize the field of computer vision and enable the development of more efficient, flexible, and human-like visual perception systems.

Some noteworthy papers in this area include: Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception, which introduces a general framework for adaptive vision models. Region-Aware Deformable Convolutions, which proposes a new convolutional operator that enhances neural networks' ability to adapt to complex image structures. Attention Schema-based Attention Control, which integrates the attention schema concept into artificial neural networks to enhance system efficiency.