Applied Patch-Based Deep Learning for Enhanced Computer Vision
Image recognition and analysis have seen great advancements in recent years due to the development of deep learning techniques. However, there remain some challenges and limitations that need to be addressed. With the increasing complexity and diversity of modern computer vision tasks, such as object recognition, image segmentation, and scene understanding, deep learning models often require a substantial amount of labelled training data to achieve satisfactory performance. Collecting and annotating such data can be time-consuming, expensive, and in some cases, impractical, particularly in domains where expert annotations are needed. Moreover, images are increasing in resolution which increases the information density of such medium. A high-resolution image contains a large amount of information, making it challenging for traditional deep learning models to capture the global context effectively.
Another challenge is the ability of deep learning models to generalise well to new, unseen data or adapt to domain shift which can introduce more local variations in the image space. These local variations can be an inherent characteristic of the data domain. For instance, medical imaging often experiences significant variations within specific classes of images. These fluctuations are primarily caused by differences in patient demographics, imaging protocols, and disease manifestations. This variation can lead to a decrease in model performance when applied to new data that deviates from the training data distribution.
Based on these challenges, we can identify a need for methods that can provide a robust and efficient approach to capturing the global context of image data, while addressing the limitations of traditional deep learning models.
While deep learning has enabled remarkable progress, computer vision systems still exhibit major limitations constraining real-world applicability across domains. Key issues persist around handling of local variability, representation efficiency, multi-scale contextual reasoning, and flexibility across conditions and tasks. Most techniques operate on full images, missing fine-grained nuances and causing ambiguity. Global feature learning entails redundancy, hindering deployment on limited hardware. Context modeling is restricted, leading to semantic errors. Inflexibility to new data distributions and applications also limits robustness and versatility.
Solving these bottlenecks is imperative to unlock the full potential of artificial visual intelligence. Enriched representations that capture subtle local details, efficient modular parts-based features, unified encoding of parts and wholes, and generalizability across environments and tasks are needed. Developing computational methodologies to address these gaps could significantly advance computer vision's ability to handle the intricacies of real-world vision.
