Deep Aggregation Network Research Articles

DAEiS-Net: Deep Aggregation Network with Edge Information Supplement for Tunnel Water Stain Segmentation.

Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information extraction and boundary information loss in complex environments. To address these challenges, this paper proposes a method called Deep Aggregation Network with Edge Information Supplement (DAEiS-Net) for detecting tunnel water stains. The proposed method employs a classic encoder-decoder architecture. Specifically, in the encoder part, a Deep Aggregation Module (DAM) is introduced to enhance feature representation capabilities. Additionally, a Multiscale Cross-Attention Module (MCAM) is proposed to suppress noise in the shallow features and enhance the texture information of the high-level features. Moreover, an Edge Information Supplement Module (EISM) is designed to mitigate semantic gaps across different stages of feature extraction, improving the extraction of water stain edge information. Furthermore, a Sub-Pixel Module (SPM) is proposed to fuse features at various scales, enhancing edge feature representation. Finally, we introduce the Tunnel Water Stain Dataset (TWS), specifically designed for tunnel water stain segmentation. Experimental results on the TWS dataset demonstrate that DAEiS-Net achieves state-of-the-art performance in tunnel water stain segmentation.

Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution

Cultural artifacts found underwater are located in complex environments with poor imaging conditions. In addition, the artifacts themselves present challenges for automated object detection owing to variations in their shape and texture caused by breakage, stacking, and burial. To solve these problems, this paper proposes an underwater cultural object detection algorithm based on the deformable deep aggregation network model for autonomous underwater vehicle (AUV) exploration. To fully extract the object feature information of underwater objects in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. In addition, the approach also incorporates a BAM module for feature optimization, which enhances the potential feature information of the object while weakening the background interference. Finally, the object prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validated and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, our method has been practically deployed on an AUV. In the field testing over a shipwreck site, the artifact detection frame rate reached up to 18 fps, which satisfies the real-time object detection requirement.

Large-Scale Nodes Classification With Deep Aggregation Network

The most fundamental task of network representation learning (NRL) is nodes classification which requires an algorithm to map nodes to vectors and use machine learning models to predict nodes' labels. Recently, many methods based on neighborhood aggregation have achieved brilliant results in this task. However, the recursive expansion of neighborhood aggregation poses scalability and efficiency problems for deep models. Existing methods are limited to shallow architectures and cannot capture the high order proximity in networks. In this article, we propose the deep aggregation network (DAN). DAN uses a layer-wise greedy optimization strategy which stacks several sequential trained base models to form the final deep model. The high order neighborhood aggregation is performed in a dynamic programming manner, which allows the recursion nature of neighborhood aggregation to be eliminated. The reverse random walk is also proposed, and combined with the classic random walk in formulating a novel sampling strategy that allows DAN to flexibly adapt to different tasks related to communities or structural roles. DAN is more efficient and effective than previous neighborhood aggregation based methods, especially when it is intended to handle large-scale networks with dense connections. Extensive experiments are conducted on both synthetic and real-world networks to empirically demonstrate the effectiveness and efficiency of the proposed method.

Massive-Scale Genre Communities Learning Using a Noise-Tolerant Deep Architecture

Accurately categorizing million-scale Internet users (e.g., Flickr or Google Picasa) into multiple communities based on their genre tastes is an indispensable techniquein machine learning and multimedia. It can facilitate a series of applications, such as fashion recommendation and 3D non-realistic photo rendering. Conventional methods cannot handle this task appropriately because of the inherent contaminated image labels, which are produced by auxiliary image label predictors. In this article, we propose a noise-tolerant deep architecture which optimally encodes stable templates, 1 1 Stable template is our proposed new concept. It denotes the distribution of co-occurring semantic categories toward an image set. Both theoretical and empirical analysises have demonstrated that such distribution (i.e. stable templates) remains almost unchanged in the presence of contaminated image tags. discovered from a collection of images with contaminated semantic labels. Specifically, we first construct a semantic space by encoding image labels using manifold embedding. Afterward, we observe that in the semantic space, the distribution of superpixels from images with the same label remains stable, regardless of the noises from image labels. According to this observation, a probabilistic generative model (Hidden Stable Analysis) is proposed to learn the stable templates toward each image label. To globally represent the composition of a user's images, a deep aggregation network is developed which statistically concatenates the CNN features learned from all its generated stable templates. Subsequently, an affinity graph is built, in which the genre difference among users is determined by their deep features. Finally, we employ a dense subgraph discovery technique which effectively mines the communities toward various genre tastes. Experiments on a million-scale image set ( $>$ 1.4 million) compiled from Flickr have demonstrated the effectiveness of our method. Additionally, empirical study on the 33 SIFT-flow categories have shown that the detected stable templates maintain almost unchanged under nearly 32% contaminated image labels.