Instance Search via Instance Level Segmentation and Feature Representation
This work addresses the challenge of effective feature representation for instance search, which is an incremental improvement in computer vision.
The paper tackles the problem of instance search by proposing an instance-level feature representation based on fully convolutional instance-aware segmentation, achieving superior performance in distinctiveness and scalability on a custom dataset.
Instance search is an interesting task as well as a challenging issue due to the lack of effective feature representation. In this paper, an instance level feature representation built upon fully convolutional instance-aware segmentation is proposed. The feature is ROI-pooled from the segmented instance region. So that instances in various sizes and layouts are represented by deep features in uniform length. This representation is further enhanced by the use of deformable ResNeXt blocks. Superior performance is observed in terms of its distinctiveness and scalability on a challenging evaluation dataset built by ourselves. In addition, the proposed enhancement on the network structure also shows superior performance on the instance segmentation task.