During the creation of graphic designs, individuals inevitably spend a lot of time and effort on adjusting visual attributes (e.g., positions, colors, and fonts) of elements to make them more aesthetically pleasing. It is a trial-and-error process, requires repetitive edits, and relies on good design knowledge. In this work, we seek to alleviate such difficulty by automatically suggesting aesthetic improvements, i.e., taking an existing design as the input and generating a refined version with improved aesthetic quality as the output. This goal presents two challenges: proposing a refined design based on the user-given one, and assessing whether the new design is better aesthetically. To cope with these challenges, we propose a design principle-guided candidate generation stage and a data-driven candidate evaluation stage. In the candidate generation stage, we generate candidate designs by leveraging design principles as the guidance to make changes around the existing design. In the candidate evaluation stage, we learn a ranking model upon a dataset that can reflect humans' aesthetic preference, and use it to choose the most aesthetically pleasing one from the generated candidates. We implement a prototype system on presentation slides and demonstrate the effectiveness of our approach through quantitative analysis, sample results, and user studies.

To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data.Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer.However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language.In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language.The proposed method works for both single-source and multi-source crosslingual NER.For the latter, we further propose a similarity measuring method to better weight the supervision from different teacher models.Extensive experiments for 3 target languages on benchmark datasets well demonstrate that our method outperforms existing state-of-theart methods for both single-source and multisource cross-lingual NER.

Prior work in cross-lingual named entity recognition (NER) with no/little labeled data falls into two primary categories: model transfer- and data transfer-based methods. In this paper, we find that both method types can complement each other, in the sense that, the former can exploit context information via language-independent features but sees no task-specific information in the target language; while the latter generally generates pseudo target-language training data via translation but its exploitation of context information is weakened by inaccurate translations. Moreover, prior work rarely leverages unlabeled data in the target language, which can be effortlessly collected and potentially contains valuable information for improved results. To handle both problems, we propose a novel approach termed UniTrans to Unify both model and data Transfer for cross-lingual NER, and furthermore, leverage the available information from unlabeled target-language data via enhanced knowledge distillation. We evaluate our proposed UniTrans over 4 target languages on benchmark datasets. Our experimental results show that it substantially outperforms the existing state-of-the-art methods.

The dominant approaches for named entity recognitionm (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast, convolutional neural networks (CNN) can fully exploit the GPU parallelism with their feedforward architectures. However, little attention has been paid to performing NER with CNNs, mainly owing to their difficulties in capturing the long-term context information in a sequence. In this paper, we propose a simple but effective CNN-based network for NER, i.e., gated relation network (GRN), which is more capable than common CNNs in capturing long-term context. Specifically, in GRN we firstly employ CNNs to explore the local context features of each word. Then we model the relations between words and use them as gates to fuse local context features into global ones for predicting labels. Without using recurrent layers that process a sentence in a sequential manner, our GRN allows computations to be performed in parallel across the entire sentence. Experiments on two benchmark NER datasets (i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve state-of-the-art performance with or without external knowledge. It also enjoys lower time costs to train and test.

Effectively coding multiview visual content is an indispensable research topic because multiview image and video that provide greatly enhanced viewing experiences often contain huge amounts of data. Generally, conventional hybrid predictive-coding methodologies are adopted to address the compression by exploiting the temporal and interviewpoint redundancy existing in a multiview image or video sequences. However, their key yet time-consuming component, motion estimation (ME), is usually not efficient in interviewpoint prediction or disparity estimation (DE), because interviewpoint disparity is completely different from temporal motion existing in the conventional video. Targeting a generic fast DE framework for interviewpoint prediction, we propose a novel DE technique in this paper to accelerate the disparity search by employing epipolar geometry. Theoretical analysis, optimal disparity vector distribution histograms, and experimental results show that the proposed epipolar geometry-based DE can greatly reduce search region and effectively track large and irregular disparity, which is typical in convergent multiview camera setups. Compared with the existing state-of-the-art fast ME approaches, our proposed DE can obtain a similar coding efficiency while achieving a significant speedup for interviewpoint prediction and coding. Moreover, a robustness study shows that the proposed DE algorithm is insensitive to the epipolar geometry estimation noise. Hence, its wide application for multiview image and video coding is promising

Many existing multiview image/video coding techniques remove inter-viewpoint redundancy by applying disparity compensation in a conventional video coding framework, e.g., H.264/MPEG-4 AVC. However, conventional methodology works ineffectively as it ignores the special characteristics of inter-viewpoint disparity. In this paper, we propose a geometric prediction methodology for accurate disparity vector (DV) prediction, such that we can largely reduce the disparity compensation cost. Based on the new DV predictor, we design a basic framework that can be implemented in most existing multiview image/video coding schemes. We also use state-of-the-art H.264/MPEG-4 AVC as an example to illustrate how the proposed framework can be integrated with conventional video coding algorithms. Our experiments show proposed scheme can effectively tracks disparity and greatly improves coding performance. Compared with H.264/MPEG-4 AVC codec, our scheme outperforms maximally 1.5 dB when encoding some typical multiview image sequences. We also carry out an experiment to evaluate the robustness of our algorithm. The results indicate our method is robust and can be used in practical applications.

Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of pairs of principal axes of people is constructed according to the relationship between "ground-points" of people detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties: 1) Camera calibration is not needed. 2) Accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise. 3) Based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency, and robustness of our method.

This paper aims at tracking vehicles from monocular intensity image sequences and presents an efficient and robust approach to three-dimensional (3-D) model-based vehicle tracking. Under the weak perspective assumption and the ground-plane constraint, the movements of model projection in the two-dimensional image plane can be decomposed into two motions: translation and rotation. They are the results of the corresponding movements of 3-D translation on the ground plane (GP) and rotation around the normal of the GP, which can be determined separately. A new metric based on point-to-line segment distance is proposed to evaluate the similarity between an image region and an instantiation of a 3-D vehicle model under a given pose. Based on this, we provide an efficient pose refinement method to refine the vehicle's pose parameters. An improved EKF is also proposed to track and to predict vehicle motion with a precise kinematics model. Experimental results with both indoor and outdoor data show that the algorithm obtains desirable performance even under severe occlusion and clutter.

Multicamera correspondence of moving people is a relatively new issue in computer vision. To cope with it, we propose a simple but effective method based on the principal axis of human body. We apply the method to real video sequences in outdoor environments. The experimental results have demonstrated the efficiency of the proposed method.

Pose evaluation is a fundamental issue in image processing and computer vision. In this paper, we propose a new method called BCE for pose evaluation based on Bayesian classification error. Various image cues are incorporated to depict an object including object shape, side region statistics and temporal information. Then a PEF (Pose Evaluation Function) is constructed based on Bayesian classification error, and an efficient algorithm to calculate it is developed. We test our new method with real outdoor image sequences, and use two criteria to compare it with two other representative ones. It is shown that our new method leads to better performance with respect to localization accuracy and robustness against general clutter and occlusion. 1

We propose a novel algorithm for efficient and robust pose determination of vehicles in traffic scenes from single monocular intensity images using calibrated cameras. We consider the pose determination process as a series of evolutions from initial pose to correct pose in 3D space, which can be decomposed into two independent 3D motions: translation and rotation. The translation parameters are obtained based on point-to-line-segment distance (PLS distance), while the rotation parameters are determined by geometric relationships among a set of specially constructed but imaginary planes. Closed-form solutions to both sub-problems are obtained, thus avoiding the usual shortcoming of relatively high computational cost of traditional 3D-model based approaches. In addition, vertex neighborhood constraint (VNC) is introduced to improve the robustness of the method. Experimental results show that the algorithm works well even under severe occlusion and clutter.