Computer vision faces several open problems that hinder its effectiveness and generalization across different applications. One major issue is generalization across datasets and domains. Models trained on one dataset or environment often struggle to perform well on others, especially when conditions such as lighting, object types, or background scenes change. This makes it difficult to develop systems that work reliably in real-world, dynamic environments. Another problem is 3D understanding. While 2D image recognition has seen significant progress, extracting and interpreting 3D information from images remains challenging. Tasks like depth estimation, scene reconstruction, and interpreting complex spatial relationships between objects are still areas of active research. Additionally, interpretability and explainability are ongoing challenges. Deep learning models, particularly CNNs, often function as "black boxes," and understanding why a model makes a certain prediction is not always clear. This limits their application in high-stakes fields like medical imaging and autonomous driving, where human oversight is crucial. Finally, handling occlusion and partial views is a common problem in object detection and recognition. Objects can be partially obscured by other objects, making it difficult for models to recognize them accurately. Developing models that can handle occlusions and recognize objects from partial or incomplete visual information remains an open problem.
What are the major open problems in computer vision?
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