The current Siamese network based on region proposal network (RPN) has attracted great attention in visual tracking due to its excellent accuracy and high efficiency. However, the design of the RPN involves the selection of the number, scale, and aspect ratios of anchor boxes, which will affect the applicability and convenience of the model. Furthermore, these anchor boxes require complicated calculations, such as calculating their intersection-over-union (IoU) with ground truth bounding boxes. Due to the problems related to anchor boxes, we propose a simple yet effective anchor-free tracker (named Siamese corner networks, SiamCorners), which is end-to-end trained offline on large-scale image pairs. Specifically, we introduce a modified corner pooling layer to convert the bounding box estimate of the target into a pair of corner predictions (the bottom-right and the top-left corners). By tracking a target as a pair of corners, we avoid the need to design the anchor boxes. This will make the entire tracking algorithm more flexible and simple than anchor-based trackers. In our network design, we further introduce a layer-wise feature aggregation strategy that enables the corner pooling module to predict multiple corners for a tracking target in deep networks. We then introduce a new penalty term that is used to select an optimal tracking box in these candidate corners. Finally, SiamCorners achieves experimental results that are comparable to the state-of-art tracker while maintaining a high running speed. In particular, SiamCorners achieves a 53.7% AUC on NFS30 and a 61.4% AUC on UAV123, while still running at 42 frames per second (FPS).

The traditional fashion industry is heavily dependent on designers whose talent and vision have a significant impact on their innovative designs. Through taking advantage of recent advances in image-to-image translation by generative adversarial networks (GANs), marked improvement in designers efficiency is now possible. Considering both randomness and controllability in the design process, this article presents a novel artificial intelligence (AI)-based framework for fashion design. Under this framework, a sketch-generation module which is based on latent space is firstly introduced for designing various sketches. Secondly, a rendering-generation module is proposed to learn mapping between textures and sketches to complete the task of fashion design. In order to achieve effectiveness in synthesizing semantic-aware textures on sketches, a multi-conditional feature interaction module is developed in the rendering-generation model. Moreover, two different training schemes are introduced to optimize both the sketch-generation module and the rendering-generation module. In order to evaluate the performance of our proposed models, we built a large-scale dataset which consists of 115,584 pairs of fashion item images. Experimental results demonstrate the effectiveness of our proposed method, and indicate that our model can facilitate designers design process by taking full advantage of the controllability of different conditions (e.g., sketch and texture) and the randomness of latent space.

Recent works on video salient object detection have demonstrated that directly transferring the generalization ability of image-based models to video data without modeling spatial-temporal information remains nontrivial and challenging. Considering both intraframe accuracy and interframe consistency of saliency detection, this article presents a novel cross-attention based encoder-decoder model under the Siamese framework (CASNet) for video salient object detection. A baseline encoder-decoder model trained with Lovász softmax loss function is adopted as a backbone network to guarantee the accuracy of intraframe salient object detection. Self- and cross-attention modules are incorporated into our model in order to preserve the saliency correlation and improve intraframe salient detection consistency. Extensive experimental results obtained by ablation analysis and cross-data set validation demonstrate the effectiveness of our proposed method. Quantitative results indicate that our CASNet model outperforms 19 state-of-the-art image- and video-based methods on six benchmark data sets.

This paper presents a character-level sequence-tosequence learning method, RNNembed. This method allows the system to read raw characters, instead of words generated by preprocessing steps, into a pure, single neural network model under an end-to-end framework. Specifically, we embed a Recurrent Neural Network (RNN) into an encoder-decoder framework and generate character-level sequence representation as input. The dimension of input feature space can be significantly reduced as well as avoiding the need to handle unknown or rare words in sequences. In the language model, we improve the basic structure of a Gated Recurrent Unit (GRU) by adding an output gate, which is used for filtering out unimportant information involved in the attention scheme of the alignment model. Our proposed method was examined in a large-scale dataset on an Englishto- Chinese translation task. Experimental results demonstrate that the proposed approach achieves a translation performance comparable, or close, to conventional word-based and phrasebased systems.