Object Augmentation for Out-of-Context Object Recognition

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2022-5-18

Author

Eryüksel, Oğul Can

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The visual context in an image contains rich information about and between foreground objects and the background. Deep learning models learn contextual information implicitly in general. However, since training datasets generally do not include all possible contexts, deep models tend to memorize contextual details. This can lead to poor recognition performance when models are deployed in real-world applications since objects may appear in unexpected contexts or places. These types of objects are called out-of-context objects. In this work, we propose an object-level augmentation framework for more robust recognition of out-of-context objects. Our proposed augmentation methodology applies object removal and object placement operations to images during the training phase. Moreover, we proposed a contrastive learning pipeline using object-level augmentations to increase performance further. Our results show that, by using object-level augmentations and contrastive learning, the out-of-context recognition performance of models can increase without losing performance on regular images. To analyze the effectiveness of the proposed method, we conducted a series of experiments for a multi-label image classification problem on the MS COCO dataset. Moreover, we provide a tool to generate images with out-of-context objects using the proposed augmentation framework.

Subject Keywords

Deep learning, Computer Vision, out-of-context objects, object removal, object placement, augmentation, multi-label classification

URI

https://hdl.handle.net/11511/97753

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

O. C. Eryüksel, “Object Augmentation for Out-of-Context Object Recognition,” M.S. - Master of Science, Middle East Technical University, 2022.