Nature | ApdativeNN：建模类人自适应感知机制，突破机器视觉「不可能三角」

Core Insights - The article discusses the significant advancements in computer vision and the challenges faced in deploying high-precision models in resource-constrained environments, such as robotics and autonomous driving, due to increased computational demands and energy consumption [2][3]. - It highlights the limitations of existing global representation learning paradigms, which process all pixels of an image or video simultaneously, leading to inefficiencies in energy and computational resources [3]. - The article introduces the AdaptiveNN architecture, which emulates human-like adaptive vision by modeling visual perception as a sequential decision-making process, allowing for efficient and flexible machine visual perception [7][11]. Group 1: Challenges in Current Computer Vision Models - High-precision models require activation of millions of parameters, resulting in increased power consumption, storage needs, and response delays, making them difficult to deploy in real-world applications [2]. - The global parallel computation paradigm leads to a significant energy efficiency bottleneck, as the computational complexity grows with the input size, making it challenging to balance high-resolution input, performance, and efficient inference [3]. Group 2: Insights from Human Visual System - Human vision operates through selective sampling of key areas rather than processing all visual information at once, which significantly reduces computational overhead and allows for efficient functioning even in resource-limited scenarios [5]. - The concept of "active observation" proposed by researchers emphasizes the need for AI systems to adopt a human-like approach to visual perception, focusing on task-driven observation [5]. Group 3: Introduction of AdaptiveNN - AdaptiveNN architecture models visual perception as a multi-step sequential decision process, allowing the model to focus on specific areas of interest and accumulate information progressively [11]. - The architecture combines representation learning with self-rewarding reinforcement learning, enabling the model to optimize its attention and decision-making without additional supervision [15][16]. Group 4: Performance and Efficiency of AdaptiveNN - In extensive experiments, AdaptiveNN achieved up to 28 times reduction in inference costs while maintaining accuracy comparable to traditional static models, demonstrating its potential for efficient visual perception [7][22]. - The model's attention mechanism automatically focuses on discriminative regions, enhancing interpretability and aligning closely with human visual behavior [22][26]. Group 5: Broader Implications and Future Research - The findings from AdaptiveNN provide insights into cognitive science, particularly in understanding human visual behavior and the mechanisms behind visual decision-making [25]. - The architecture's application in embodied intelligence models shows significant improvements in reasoning and perception efficiency, suggesting a promising direction for future research in AI and cognitive science [29].