Salient object detection is a binary pixel-wise classification to distinguish objects in an image, and also have attracted many research interests in the optical Remote Sensing Images (RSIs). The existing state-of-the-art method exploits the full encoder-decoder architecture to predict salient objects in the optical RSIs, suffering from the problem of unsmooth edges and incomplete structures. To address these problems, in this paper, we propose a Boundary-Aware Network (BANet) with two-stage partial decoders sharing the same encoders for salient object detection in RSIs. Specifically, a Boundary-Aware Partial Decoder (BAD) is introduced at the first stage to focus on learning clear edges of salient objects. To solve the pixel-imbalance problem between boundary and background, an edge-aware loss is proposed to guide learning the BAD network. The resulting features are then employed in turn to enhance high-level features. Afterwards, the Structure-Aware Partial Decoder (SAD) is further introduced at the second stage to improve the structure integrity of salient objects. To alleviate the problem of incomplete structures, the structural similarity loss is further proposed to supervise learning the SAD network. In a consequence, our proposed BANet can predict salient objects with clear edges and complete structure, while reducing model parameters due to the discardment of low-level features. Besides, training a deep neural network requires a large amount of images, and the current benchmark datasets for optical remote sensing images are not large enough. Therefore, we also create a large-scale challenging dataset for salient object detection in RSIs. Extensive experiments demonstrate that our proposed BANet outperforms previous RSI SOD models on all existing benchmark datasets and our new presented dataset available at https://github.com/QingpingZheng/RSISOD.

The classical person re-identification methods are mostly focused on employing discriminative features amongst which the distance is measured on Euclidean space, while the effort of re-ranking is constrained as the lack of the utilization of quality context representation in embedding set. In this paper, we incorporate graph models on feature subsets resorting to the initial ranking by adopting the integration of the attention mechanism into graph convolution network. On the one hand, the context information regarding embedding pairs is considered to compute feature group similarity through the aggregation operation by using graph convolution networks. On the other hand, we adopt a channel attention mechanism to enhance the contribution of relevant feature channels, further strengthening the ability of similarity pulling and dissimilarity pushing of the overall network. Experimental study shows that the proposed network structure is superior to the state-of-the-art deep neural networks on three very challenging datasets that are popular in examining person re-identification techniques.

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing.

Two dimensional strip packing problem implies a sort of NP-hard combinational optimization, where a set of rectangles are defined all for filling a strip with finite width and infinite height being minimized. Optimization taking no account of rotation and guillotine constraints remains problematic in solving the problem. This paper proposes a new variant of a hybrid heuristic algorithm for optimizing solutions of the strip packing problem. This algorithm involves three key improvements: the building of δ scoring rules for selecting rectangles, the use of the red-black trees that stores rectangle indices for quickly locating the most suitable rectangles, and the embedding of a hierarchical method into a random local search to implement an optimization solution. Comparative studies show that the proposed algorithm variant outperforms state-of-the-art algorithms on almost all benchmark datasets.