Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD). Then we explore a pathway of cracking these nuts from the perspective of data augmentation. Primarily, a particular copy- paste scheme is proposed towards making crowded scenes. Based on this operation, we first design a "consensus learning" method to further resist the ICD problem and then find out the pasting process naturally reveals a pseudo "depth" of object in the scene, which can be potentially used for alleviating CDD dilemma. Both methods are derived from magical using of the copy-pasting without extra cost for hand-labeling. Experiments show that our approach can easily improve the state-of-the-art detector in typical crowded detection task by more than 2% without any bells and whistles. Moreover, this work can outperform existing data augmentation strategies in crowded scenario.

Detecting pedestrians and their associated faces jointly is a challenging task. On one hand, body or face could be absent because of occlusion or non-frontal human pose. On the other hand, the association becomes difficult or even miss-leading in crowded scenes due to the lack of strong correlational evidence. This paper proposes Body-Face Joint (BFJ) detector, a novel framework for detecting bodies and their faces with accurate correspondance. We follow the classical multi-class detector design by detecting body and face in parallel but with two key contributions. First, we propose an Embedding Matching Loss (EML) to learn an associative embedding for matching body and face of the same person. Second, we introduce a novel concept, "head hook", to bridge the gap of matching body and faces spatially. With the new semantical and geometrical sources of information, BFJ greatly reduces the difficulty of detecting body and face in pairs. Since the problem is unexplored yet, we design a new metric named log-average miss matching rate (mMR−2) to evaluate the association performance and extend the CrowdHuman and CityPersons benchmarks by annotating each face box. Experiments show that our BFJ detector can maintain state-of-the-art performance in pedestrian detection on both one-stage and two-stage structures while greatly outperform various body-face association strategies. Code will be available at: https://github.com/AibeeDetect/BFJDet.

In this letter, we propose a novel supervised change detection method based on a deep siamese convolutional network for optical aerial images. We train a siamese convolutional network using the weighted contrastive loss. The novelty of the method is that the siamese network is learned to extract features directly from the image pairs. Compared with hand-crafted features used by the conventional change detection method, the extracted features are more abstract and robust. Furthermore, because of the advantage of the weighted contrastive loss function, the features have a unique property: the feature vectors of the changed pixel pair are far away from each other, while the ones of the unchanged pixel pair are close. Therefore, we use the distance of the feature vectors to detect changes between the image pair. Simple threshold segmentation on the distance map can even obtain good performance. For improvement, we use a k-nearest neighbor approach to update the initial result. Experimental results show that the proposed method produces results comparable, even better, with the two state-of-the-art methods in terms of F-measure.