Object Classification

Object classification, the task of assigning objects to predefined categories, is a core problem in computer vision with applications ranging from robotics to medical diagnosis. Current research emphasizes improving robustness and efficiency, focusing on techniques like continual learning to adapt to new data streams, ensemble methods to combine diverse models, and the use of graph convolutional networks and other deep learning architectures to extract relevant features from various data modalities (e.g., images, point clouds, radar data). These advancements are crucial for developing more reliable and adaptable systems in diverse real-world scenarios, particularly those with limited labeled data or challenging conditions like occlusions or noisy inputs.