Contextual Relabelling of Detected Objects
This work addresses the challenge of improving object detection accuracy for computer vision applications, but it appears incremental as it builds on existing Faster RCNN methods.
The authors tackled the problem of improving object detection by leveraging contextual information, such as object co-occurrence and spatial relationships, and demonstrated that their contextual models enhanced Faster RCNN-based detection performance on the MSCOCO dataset.
Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects provides deep and complex information about scenes. It also can play an important role in improving object detection. In this work, we present two contextual models (rescoring and re-labeling models) that leverage contextual information (16 contextual relationships are applied in this paper) to enhance the state-of-the-art RCNN-based object detection (Faster RCNN). We experimentally demonstrate that our models lead to enhancement in detection performance using the most common dataset used in this field (MSCOCO).