The integration of knowledge graphs with Residual Networks (ResNets) marks a transformative shift in computer vision, moving beyond mere pattern recognition toward true understanding. Since ResNet’s introduction in 2015, which solved the vanishing gradient problem and enabled deeper networks, the field has evolved to address a core limitation: neural networks lack explicit reasoning about relationships, context, and semantics. Knowledge graph-enhanced ResNets bridge this gap by combining the perceptual power of deep learning with the structured reasoning of symbolic AI. These hybrid systems significantly improve performance in visual reasoning tasks, delivering 10–15% accuracy gains over standard ResNets. They also enhance interpretability—models can explain decisions by referencing known relationships, such as “a pedestrian is likely to cross at a crosswalk” or “a traffic light must be above the road.” This is particularly valuable in safety-critical domains like autonomous driving and medical diagnosis. The architecture integrates knowledge at multiple levels. Visual features extracted by ResNet are enriched with semantic relationships from knowledge graphs. Graph Convolutional Networks (GCNs) process these relationships, and attention mechanisms allow bidirectional flow between visual and symbolic representations. For example, a ResNet might detect a car and a traffic light, but with a knowledge graph, it understands that the light controls the car’s movement. The fusion is formalized as F(x) = GCN(x) + x, where the residual connection preserves visual features while the GCN injects relational context. Three main integration strategies have emerged. Early fusion combines image features with entity embeddings at input. Late fusion applies symbolic reasoning after neural processing. Attention-based fusion, the most advanced, dynamically aligns visual and symbolic information using multi-head attention, allowing the model to focus on relevant relationships during decision-making. In 2024, breakthroughs demonstrated the power of this approach. At CVPR, HiKER-SGG from Carnegie Mellon achieved 19.4% accuracy on scene graph detection at recall@20, far surpassing the 11.4% of baseline models. Naver AI’s EGTR framework, combining ResNet-50 with transformers, set new standards on Visual Genome and Open Image V6, earning Best Paper recognition. A practical implementation in PyTorch uses a pre-trained ResNet as a visual backbone and integrates it with a GCN via graph embeddings and attention. The model extracts visual features, retrieves a relevant subgraph based on context, processes it through GCNs, and fuses the results with visual data before classification. This approach enables the model to reason about object relationships during inference. Performance benchmarks confirm the benefits. Graph R-CNN achieves 31.6% accuracy on scene graph detection at recall@100, nearly double the 17.0% of standard methods. While computational overhead increases by 15–25%, optimizations like quantization and TensorRT inference are reducing this gap. Real-world applications are already impactful. In medical imaging, integrating ResNet with the UMLS knowledge graph improved rare disease diagnosis by 40% and cut training data needs by 60%. Bosch’s DSceneKG system uses knowledge graphs from NuScenes and Lyft data to achieve 87% precision in identifying unknown entities in driving scenes. In robotics, the roboKG framework reached 91.7% accuracy in predicting action sequences by encoding object-task relationships. Challenges remain. Knowledge acquisition is labor-intensive, with domain-specific graphs often taking months to build. Computational costs and memory usage are higher, though sparse representations and dynamic graph pruning are helping. Ensuring consistency and accuracy in large-scale knowledge extraction remains difficult. Looking ahead, dynamic graph learning—where models adaptively build knowledge on the fly—holds promise. Integration with large language models enables natural language reasoning grounded in structured knowledge. Specialized hardware from companies like Graphcore and SambaNova is emerging to accelerate graph computations, potentially eliminating performance disadvantages. This convergence represents a new paradigm in AI: intelligent vision that sees, understands, and reasons. It offers higher accuracy, reduced data dependency, and explainable decisions. For researchers and practitioners, the tools are now accessible. Experimenting with hybrid architectures today may lead to the next major leap in artificial intelligence. The future of vision is not just neural—it’s symbolic, connected, and intelligent.