What are the limitations of CNN in computer vision?

Convolutional Neural Networks (CNNs) have been widely used in computer vision, but they come with notable limitations. One key issue is their struggle with spatial invariance and hierarchical relationships. CNNs rely on pooling layers and strided convolutions to achieve translation invariance, which helps recognize objects regardless of their position. However, this process often discards precise spatial information. For example, in tasks like medical image segmentation, where the exact location of a tumor matters, pooling can reduce accuracy. Additionally, CNNs prioritize local patterns over global structures, making them less effective when objects have complex spatial dependencies. If an image contains rotated or scaled versions of an object, CNNs might fail to recognize them as the same class unless explicitly trained on such variations.

Another limitation is their inability to capture long-range dependencies and global context. CNNs process images through local receptive fields, which work well for detecting edges or textures but struggle when relationships between distant regions are critical. For instance, in scene understanding, recognizing that a “boat” is likely near “water” requires analyzing the entire image, not just local patches. While deeper CNNs expand receptive fields, this approach is computationally inefficient and still misses explicit modeling of global interactions. Transformers, with self-attention mechanisms, have addressed this gap by directly linking all pixels, but they come with their own trade-offs, such as higher memory usage. CNNs also face challenges in tasks like image captioning, where understanding context beyond local features is essential.

Finally, CNNs are data-hungry and computationally intensive. They require large labeled datasets to generalize well, which limits their use in domains with scarce or expensive-to-label data, like medical imaging or industrial defect detection. While techniques like data augmentation and transfer learning mitigate this, they don’t fully eliminate the need for substantial initial data. Additionally, deeper architectures (e.g., ResNet-152) demand significant computational resources, making them impractical for real-time applications on edge devices. Optimizations like model pruning or quantization can reduce size, but they often degrade performance. For example, deploying a CNN on a smartphone for object detection might require sacrificing accuracy to meet latency constraints. These limitations highlight the need for hybrid architectures or alternative models that balance efficiency, context awareness, and data requirements.