Text Co-detection in Multi-view Scene

Multi-View Visual Relationship Detection with Estimated Depth Map

The abundant visual information contained in multi-view images is widely used in computer vision tasks. Existing visual relationship detection frameworks have extended the feature vector to improve model performance. However, single view information can not fully reveal the visual relationships in complex visual scenes. To solve this problem and explore the multi-view information in a visual relationship detection (VRD) model, a novel multi-view VRD framework based on a monocular RGB image and an estimated depth map is proposed. The contributions of this paper are threefold. First, we construct a novel multi-view framework which fuses information of different views extracted from estimated RGB-D images. Second, a multi-view image generation method is proposed to transfer flat visual space to 3D multi-view space. Third, we redesign the visual relationship balanced classifier which can process multi-view feature vectors simultaneously. Detailed experiments were conducted on two datasets to demonstrate the effectiveness of the multi-view VRD framework. The experimental results showed that the multi-view VRD framework resulted in state-of-the-art zero-shot learning performance in specific depth conditions.

Multi-Relation Attention Network for Image Patch Matching

Deep convolutional neural networks attract increasing attention in image patch matching. However, most of them rely on a single similarity learning model, such as feature distance and the correlation of concatenated features. Their performances will degenerate due to the complex relation between matching patches caused by various imagery changes. To tackle this challenge, we propose a multi-relation attention learning network (MRAN) for image patch matching. Specifically, we propose to fuse multiple feature relations (MR) for matching, which can benefit from the complementary advantages between different feature relations and achieve significant improvements on matching tasks. Furthermore, we propose a relation attention learning module to learn the fused relation adaptively. With this module, meaningful feature relations are emphasized and the others are suppressed. Extensive experiments show that our MRAN achieves best matching performances, and has good generalization on multi-modal image patch matching, multi-modal remote sensing image patch matching and image retrieval tasks.