Recently, recognition of handwritten mathematical expression has been greatly improved by employing sequence modeling methods such as encoder-decoder based methods. Existing encoder-decoder models use string decoders or tree decoders to generate markup of mathematical expression recognition. String decoders directly generate LaTeX strings and tree decoders decode expressions into tree structures. The generalization of string decoders is poor on mathematical expressions with complex hierarchical structures, but its language model is better. Tree decoders can deal with the complex hierarchical structures, but its language model is weakened. In order to take advantage of the above two decoders, we propose a novel structural string decoder (SSD) which not only has good generalization but also can make good use of language model. We demonstrate how the proposed SSD outperforms state-of-the-art string decoders and tree decoders through a set of experiments on CROHME database, which is currently the largest benchmark for online handwritten mathematical expression recognition.

Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, which inevitably restricts its generality. Deep learning techniques may help to address this issue, but the challenging problem is that we need to build a large-scale image retargeting dataset for the training of deep retargeting models. However, building such a dataset requires huge human efforts. In this paper, we propose a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image retargeting with a single deep model, without relying on any explicit user annotations. Our idea is built on the reverse mapping from the retargeted images to the given images. If the retargeted image has serious distortion or excessive loss of important visual information, the reverse mapping is unlikely to restore the input image well. We constrain this forward-reverse consistency by introducing a cyclic perception coherence loss. In addition, we propose a simple yet effective image retargeting network (IRNet) to implement the image retargeting process. Our IRNet contains a spatial and channel attention layer, which is able to discriminate visually important regions of input images effectively, especially in cluttered images. Given arbitrary sizes of input images and desired aspect ratios, our Cycle-IR can produce visually pleasing target images directly. Extensive experiments on the standard RetargetMe dataset show the superiority of our Cycle-IR.

Image retargeting aims to obtain high visual quality of target images for human vision. Through semantic segmentation and understanding of input images, we can better preserve the important semantic regions, so as to effectively improve the performance of image retargeting. Benefit from the successful application of deep neural network in the field of semantic segmentation, in this paper, we propose a novel image retargeting approach using semantic segmentation and pixel fusion. Compared with existing image retargeting methods, our approach can effectively reduce geometric distortion during image retargeting by finely reallocating scaling factors for each region based on the semantic segmentation results. Experimental results demonstrate that the proposed approach can well preserve important semantic regions while leaving less unnatural geometric distortion. Our approach also shows the important role of semantic segmentation and understanding of scenes in image retargeting in detail.

Pictures taken under the low-light condition often suffer from low contrast and loss of image details, thus an approach that can effectively improve low-light images is demanded. Traditional Retinex-based methods assume that the reflectance components of low-light images keep unchanged, which neglect the color distortion and lost details. In this paper, we propose an end-to-end learning-based framework that first decomposes the low-light image and then learns to fuse the decomposed results to obtain the high quality enhanced result. Our framework can be divided into a RDNet (Retinex Decomposition Network) for decomposition and a FENet (Fusion Enhancement Network) for fusion. Specific multi-term losses are respectively designed for the two networks. We also present a new RDGAN (Retinex Decomposition based Generative Adversarial Network) loss, which is computed on the decomposed reflectance components of the enhanced and the reference images. Experiments demonstrate that our approach is good at color and detail restoration, which outperforms other state-of-the-art methods.