Modeling Visual Context is Key to Augmenting Object Detection Datasets (1807.07428v1)

Abstract: Performing data augmentation for learning deep neural networks is well known to be important for training visual recognition systems. By artificially increasing the number of training examples, it helps reducing overfitting and improves generalization. For object detection, classical approaches for data augmentation consist of generating images obtained by basic geometrical transformations and color changes of original training images. In this work, we go one step further and leverage segmentation annotations to increase the number of object instances present on training data. For this approach to be successful, we show that modeling appropriately the visual context surrounding objects is crucial to place them in the right environment. Otherwise, we show that the previous strategy actually hurts. With our context model, we achieve significant mean average precision improvements when few labeled examples are available on the VOC'12 benchmark.

• The paper introduces a novel augmentation strategy that leverages segmentation and context-aware object placement to enrich training data.
• It employs a CNN-based visual context model to determine optimal object placement within an image, ensuring environment consistency.
• Validated on PASCAL VOC, the method achieves a 4% overall mAP improvement and over 5% gains for context-sensitive categories like aeroplanes and birds.

Contextual Data Augmentation for Object Detection

The paper "Modeling Visual Context is Key to Augmenting Object Detection Datasets" by Nikita Dvornik, Julien Mairal, and Cordelia Schmid introduces a novel approach for data augmentation in object detection, emphasizing the significance of visual context in enhancing training datasets. Traditional data augmentation techniques often involve simple geometric and color transformations, which, while effective, do not fully leverage the rich contextual information present in images. This paper proposes a methodology that exploits segmentation annotations to artificially increase the number of object instances in training data, combined with a context-sensitive object placement strategy to improve the training of object detection models.

Key Contributions

1. Contextual Data Augmentation: The authors propose an augmentation technique that involves augmenting object detection datasets by strategically placing additional segmented object instances within training images. The distinguishing factor of their approach lies in the integration of a visual context model that predicts appropriate placement locations for these objects, thereby maintaining contextual integrity.
2. Visual Context Model: A convolutional neural network is trained to assess the suitability of a potential placement location for a given object class within an image. This model, informed by neighborhood features surrounding potential placements, ensures that objects are contextually harmonized with their environment, mitigating the detrimental effects of random object placement traditionally seen in data augmentation.
3. Performance Benchmarks: The approach is validated on the PASCAL VOC 2012 dataset with the VOC07-test set as the evaluation metric, demonstrating notable improvements in mean average precision (mAP) over baseline object detection models that do not employ context-aware data augmentation. Particularly, significant gains were observed for categories sensitive to contextual information, like aeroplanes and birds.

Numerical Results

The experimental results underscore that contextually-driven data augmentation confers more substantial benefits to object detection models compared to basic data augmentation strategies. The paper notes a 4% improvement on average in detection accuracy when applying their context-aware augmentation. Additionally, the research highlights specific object categories such as aeroplanes, cats, and horses, which benefited from the intricate visual context modeling by more than 5% in mAP, proving the method's efficacy in scenarios with limited labeled data.

Implications and Future Work

The paper’s findings offer both practical and theoretical implications. Practically, the introduction of contextual data augmentation addresses a core limitation in current object detection datasets by enhancing robustness against overfitting when labeled data is scarce. Theoretically, elucidating the role of context aligns with broader trends in AI research emphasizing the interconnectedness of objects and scenes. Future developments could explore the integration of this context modeling framework into other computer vision tasks such as scene segmentation and contextualized scene generation. Furthermore, leveraging automatic segmentation to extend this methodology to datasets lacking segmentation annotations represents a promising avenue for further research.

In conclusion, this work marks a strategic step forward in augmenting object detection training data by acknowledging and incorporating the critical role of visual context. Such advancements not only push the boundaries of object detection but also open doors to a richer understanding of scene content in computer vision systems.