The legal charge prediction task aims to judge appropriate charges according the given fact description in cases. Most existing methods formulate it as a multi-class text classification problem and have achieved tremendous progress. However, performance on low-frequency is still unsatisfactory. Previous studies indicate leveraging label information can facilitate this task, but approaches utilizing are not fully explored. In paper, inspired by vision-language fusion techniques multi-modal...

This paper presents an efficient algorithm to track the object contour in image sequences. Firstly, probability distributions of color feature and texture are approximated by different schemes. Secondly, background pixels distinguished calculating their posterior light Bayesian formula based on two models. Finally, energy functional is proposed solved gradient descent flow evolve initial which estimated motion feature, so that minimum corresponds contour. In order save computational...

This paper presents a three-step framework to remove the highlight exists on objects in certain conditions. Unlike traditional HDR (High Dynamic Range) technology requires multiple registrated image sequence; our method needs only two arbitrary images. SURF (Speeded Up Robust Features) matching algorithm is first applied find corresponding point pairs between images; homography then found by perspective transformation theory; minimum gray selection used at last eliminated and fuse related...

Dialogue representation and understanding aim to convert conversational inputs into embeddings fulfill discriminative tasks. Compared with free-form text, dialogue has two important characteristics, hierarchical semantic structure multi-facet attributes. Therefore, directly applying the pretrained language models (PLMs) might result in unsatisfactory performance. Recently, several work focused on dialogue-adaptive post-training (DialPost) that further trains PLMs fit dialogues. To model...

As an important and fundamental methodology in the fields of pattern recognition image processing, learning middle level feature has attracted increasing interest during recent years, where generative mapping shown highly completive performance diverse applications. In this paper, a representation is proposed based on Deep Boltzmann Machine (DBM) sufficient statistics (SS) for detection. approach, DBM employed to model data distribution hidden information inferred by together with other...

Pedestrian detection and recognition has become the basic research in various social fields. Convolutional neural networks have excellent learning ability can recognize patterns with robustness to some extent distortions transformations. Yet, they need much more intermediate hidden units cannot from unlabeled samples. In this paper, we purpose a latent training model based on convolutional network. The purposed adopts part detectors reduce scale of layer. It also follows method determine...

Texture information plays an important role in rendering true objects, especially with the wide application of image-based three-dimensional (3-D) reconstruction and 3-D laser scanning. This paper proposes a seamless texture mapping algorithm to achieve high-quality visual effect for reconstruction. At first, series image sets is produced by analyzing visibility triangular facets, are clustered segmented into number optimal reference patches. Second, generated patches sequenced create rough...

In this paper we propose a novel multiple target tracking model composed of two detectors and tracker. An on-line detector tracker are used to generate candidates, whose confidence scores then evaluated by the off-line trained detectors. data association stage, high-efficient inference in structural leads optimal result. The experimental results demonstrate that our can overcome occlusion appearance changing problems. be applied analyze information single or among targets.

Recent cross-lingual summarization research has pursued the use of a unified end-to-end model which demonstrated certain level improvement in performance and effectiveness, but this approach stitches together multiple tasks makes computation more complex. Less work focused on alignment relationships across languages, led to persistent problems summary misordering loss key information. For reason, we first simplify multitasking by converting translation task into an equal proportion so that...

In scene parsing, the model is required to be able process complex multi-modal data such as images and contexts in real scenes, discover their implicit connections from objects existing scene. As a storage method that contains entity information relationship between entities, knowledge graph can well express semantic this paper, new multi-phase was proposed solve parsing tasks; first, used align then graph-based generates results. We also designed an experiment of feature engineering’s...

With its unique information-filtering function, text summarization technology has become a significant aspect of search engines and question-and-answer systems. However, existing models that include the copy mechanism often lack ability to extract important fragments, resulting in generated content suffers from thematic deviation insufficient generalization. Specifically, Chinese automatic using traditional generation methods loses semantics because reliance on word lists. To address these...