Fully convolutional neural networks (FCNs) have shown their advantages in the salient object detection task. However, most existing FCNs-based methods still suffer from coarse object boundaries. In this paper, to solve this problem, we focus on the complementarity between salient edge information and salient object information. Accordingly, we present an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network. In the first step, we extract the salient object features by a progressive fusion way. In the second step, we integrate the local edge information and global location information to obtain the salient edge features. Finally, to sufficiently leverage these complementary features, we couple the same salient edge features with salient object features at various resolutions. Benefiting from the rich edge information and location information in salient edge features, the fused features can help locate salient objects, especially their boundaries more accurately. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on six widely used datasets without any pre-processing and post-processing. The source code is available at
Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper, we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75, 000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In addition, we annotate about 19, 000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upperlevel category. Furthermore, we perform several baseline experiments on the IP102 dataset, including handcrafted and deep feature based classification methods. Experimental results show that this dataset has the challenges of interand intra-class variance and data imbalance. We believe our IP102 will facilitate future research on practical insect pest control, fine-grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/ xpwu95/IP102.
Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in Convolutional Neural Networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning and develop a multi-task deep framework by jointly optimizing classification and distribution prediction. While the proposed method prefers to the distribution datasets with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i.e. Emotion6, Flickr LDL, Twitter LDL, and the largest single label dataset, i.e. Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.
Skin disease is one of the most common human illnesses. It pervades all cultures, occurs at all ages, and affects between 30 % and 70 % of individuals, with even higher rates in at-risk. However, diagnosis of skin diseases by observing is a very difficult job for both doctors and patients, where an intelligent system can be helpful. In this paper, we mainly introduce a benchmark dataset for clinical skin diseases to address this problem. To the best of our knowledge, this dataset is currently the largest for visual recognition of skin diseases. It contains 6,584 images from 198 classes, varying according to scale, color, shape and structure. We hope that this benchmark dataset will encourage further research on visual skin disease classification. Moreover, the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks(CNNs), we also perform extensive analyses on this dataset using the state of the art methods including CNNs.
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