Fine-to-coarse object classification of very large images

Even though deep neural networks have shown to be promising for object recognition tasks, they suffer from limitations for some real world applications: (i) their ability to recognize relatively small objects in megapixel images is limited; and (ii) they require large amounts of computations for processing high resolution data, especially in resource constraint edge devices. We develop a fine-grained attention-based neural network approach to address these shortcomings. Our model is able to capture the features for both small and large objects in high resolution image while discarding the less informative portions to reduce the computational burden. We use a likelihood-attention layer to aggregate contextually important information from various patches of the image. Experimental comparisons on four high resolution datasets demonstrate that our proposed method outperforms existing approaches in terms of accuracy and computation cost.