Abstract In ecology, deep learning is improving the performance of camera‐trap image based wild animal analysis. However, high labelling cost becomes a big challenge, as it requires involvement of huge human annotation. For example, the Snapshot Serengeti (SS) dataset contains over 900,000 images, while only 322,653 contains valid animals, 68,000 volunteers were recruited to provide image level labels such as species, the no. of animals and five behaviour attributes such as standing, resting and moving etc. In contrast, the Gold Standard SS Bounding‐Box Coordinates (GSBBC for short) contains only 4011 images for training of object detection algorithms, as the annotation of bounding‐box for animals in the image, is much more costive. Such a no. of training images, is obviously insufficient. To address this, the authors propose a method to generate bounding‐boxes for a larger dataset using limited manually labelled images. To achieve this, the authors first train a wild animal detector using a small dataset (e.g. GSBBC) that is manually labelled to locate animals in images; then apply this detector to a bigger dataset (e.g. SS) for bounding‐box generation; finally, we remove false detections according to the existing label information of the images. Experiments show that detector trained with images whose bounding‐boxes are generated using the proposal, outperformed the existing camera‐trap image based animal detection, in terms of mean average precision (mAP). Compared with the traditional data augmentation method, our method improved the mAP by 21.3% and 44.9% for rare species, also alleviating the long‐tail issue in data distribution. In addition, detectors trained with the proposed method also achieve promising results when applied to classification and counting tasks, which are commonly required in wildlife research.

Recent years have witnessed impressive advances in the area of weakly-supervised semantic segmentation (WSSS). However, most of existing approaches are based on class activation maps (CAMs), which suffer from the under-segmentation problem (i.e., objects of interest are segmented partially). Although a number of literature works have been proposed to tackle this under-segmentation problem, we argue that these solutions built on CAMs may not be optimal for the WSSS task. Instead, in this paper we propose a network based on the object-aware activation map (OAM). The proposed network, termed OAM-Net, consists of four loss functions (foreground loss, background loss, average pixel and consistency loss) which ensure exactness, completeness, compactness and consistency of segmented objects via adversarial training. Compared to conventional CAM-based methods, our OAM-Net overcomes the under-segmentation drawback and significantly improves segmentation accuracy with negligible computational cost. A thorough comparison between OAM-Net and CAM-based approaches is carried out on the PASCAL VOC2012 dataset, and experimental results show that our network outperforms state-of-the-art approaches by a large margin. The code will be available soon.

We present a two-stage learning framework for weakly supervised object localization (WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation Maps), this paper proposes to localize objects using the low-level feature based activation maps. In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner. In the second stage, we employ an evaluator to evaluate the activation maps predicted by the activation map generator. Based on this, we further propose a weighted entropy loss, an attentive erasing, and an area loss to drive the activation map generator to substantially reduce the uncertainty of activations between object and background, and explore less discriminative regions. Based on the low-level object information preserved in the first stage, the second stage model gradually generates a well-separated, complete, and compact activation map of object in the image, which can be easily thresholded for accurate localization. Extensive experiments on CUB-200-2011 and ImageNet-1K datasets show that our framework surpasses previous methods by a large margin, which sets a new state-of-the-art for WSOL. Code will be available soon.

Fast and accurate landmark location and bounding box detection are important steps in 3D medical imaging. In this paper, we propose a novel multi-task learning framework, for real-time, simultaneous landmark location and bounding box detection in 3D space. Our method extends the famous single-shot multibox detector (SSD) from single-task learning to multitask learning and from 2D to 3D. Furthermore, we propose a post-processing approach to refine the network landmark output, by averaging the candidate landmarks. Owing to these settings, the proposed framework is fast and accurate. For 3D cardiac magnetic resonance (MR) images with size 224×224×64, our framework runs ~128 volumes per second (VPS) on GPU and achieves 6.75mm average point-to-point distance error for landmark location, which outperforms both state-of-the-art and baseline methods. We also show that segmenting the 3D image cropped with the bounding box results in both improved performance and efficiency.