دانلود کتاب پیشرفت‌ها در بینایی کامپیوتر و یادگیری عمیق و کاربردهای آن

(1) Computer Vision: The field of computer vision is making significant strides in dynamic reasoning capability through test-time scaling (TTS) [1] technology. TTS optimizes the robustness and interpretability of models in complex tasks by flexibly allocating computational resources. Multimodal base models, such as CLIP (contrastive language-image pre-training) [2] and Florence, facilitate the deep fusion of vision and language through cross-modal alignment techniques. These advancements have significantly improved the accuracy of visual question answering (VQA) and cross-modal retrieval. Generative AI technologies, such as Stable Diffusion, have also broken through the limitations of 2D image generation, enabling the transition to semantics-driven 3D scene models, like neural radiance fields (NeRF) [3]. This shift supports the generation of spatial models with physically interactive attributes from a single sheet of input, providing a new paradigm for virtual reality and industrial design. In addition, the introduction of the spatial intelligence [4] concept allows computer vision systems to simulate physical interactions in 3D space, driving the development of embodied intelligence and robot navigation. However, these macroscopic technological frameworks still face several challenges, including inadequate algorithmic adaptation, high computational costs, and fragmented cross-modal representations in specific scenarios. While this Special Issue highlights significant progress in algorithmic improvements and scene adaptation, two key knowledge gaps persist. First, much of the current research is centered on the optimization of unimodal vision tasks, while exploration into multimodal alignment techniques remains relatively underdeveloped. Second, research on dynamic reasoning capabilities is still in its infancy, and existing models struggle to meet the real-time adaptive demands of complex physical interaction environments. In addition, the integration of generative AI with spatial intelligence remains insufficient, and further breakthroughs are needed to enhance the simulation of dynamic physical attributes. Future research should further integrate multimodal a priori knowledge and dynamic reasoning mechanisms. On the one hand, linguistic descriptions can be embedded into the industrial defect detection process, and a joint visual-semantic representation space can be constructed to enhance model interpretability. On the other hand, neural radial field generation techniques based on physics engines need to be explored to enhance the simulation of physical interactions within 3D models through the introduction of rigid-body dynamics constraints. In addition, for incremental SFM and UAV scene reconstruction, developing an adaptive computation offloading strategy that combines the characteristics of edge computing devices will enable real-time 3D closed-loop sensing with cloud collaboration. (2) Feature Extraction and Image Selection: Self-supervised learning and comparative learning frameworks, such as SimCLR (Simple Contrastive Learning of Visual Representations) [5] and MoCo (Momentum Contrast), have become the dominant paradigms for feature extraction. These frameworks significantly reduce the reliance on labeled data, especially in the small-sample task of medical imaging. Image selection techniques combine attention mechanisms with reinforcement learning to optimize dynamic sampling. Interpretability methods, such as the improved version of Grad-CAM++ (gradient-weighted class activation mapping) enhance the model’s credibility in highly sensitive scenarios, like remote sensing and security, by visualizing the importance of features. Compared with current mainstream self-supervised learning and comparative learning paradigms, the research presented in this Special Issue focuses on feature characterization optimization and heterogeneous data fusion in vertical scenarios. However, two knowledge gaps remain. First, at the level of basic theory, most methods fail to fully integrate the advantages of contemporary self-supervised comparative learning techniques, limiting the model’s generalization ability. Second, the dynamic optimization mechanism has not yet formed a complete closed loop, and existing image selection techniques lack a dynamic sampling strategy that integrates reinforcement learning, making it challenging to achieve the synergistic optimization of defective region detection and manual review efficiency in industrial quality inspection scenarios. In addition, although several studies have adopted visual feature analysis, interpretability methods still rely on traditional heat maps. Newer interpretable frameworks, such as the improved version of Grad-CAM++, have not been introduced, potentially limiting the certification of model credibility in high-reliability domains like remote sensing and security. Future research should deepen the exploration of three key areas: First, it is necessary to establish a deep fusion mechanism between generic feature extraction frameworks and domain-specific knowledge, and to develop a self-supervised pre-training model that requires fewer samples for pathological image analysis. Second, there is a need to build a closed-loop optimization system that is dynamically interpretable, and to form a complete cognitive chain from feature extraction to decision-making validation. Lastly, it is crucial to break through the intrinsic limitations of two-dimensional visual representation, and to develop an implicit model based on neural radiance fields (NeRFs), which represent the most effective approach to visualization. Additionally, exploring the synergistic integration of multimodal large language models with feature extraction networks will open up new directions for constructing intelligent visual systems with semantic understanding capabilities.