Information on social media is multi-modal, most of which contains the meaning of sarcasm. In recent years, many people have studied the problem of sarcasm detection. Many traditional methods have been proposed in this field, but the study of deep learning methods to detect sarcasm is still insufficient. It is necessary to comprehensively consider the information of the text,the changes of the tone of the audio signal,the facial expressions and the body posture in the image to detect sarcasm. This paper proposes a multi-level late-fusion learning framework with residual connections, a more reasonable experimental data-set split and two model variants based on different experimental settings. Extensive experiments on the MUStARD show that our methods are better than other fusion models. In our speaker-independent experimental split, the multi-modality has a 4.85% improvement over the single-modality, and the Error rate reduction has an improvement of 11.8%. The latest code will be updated to this URL later: https://github.com/DingNing123/m_fusion
相似文献Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well known, however, that deep learning models are data-hungry and often require huge volumes of annotated data in order to achieve competitive performances. And since data annotation is a costly and labor-intensive process, it remains one of the major hurdles to their practical deployment. This study investigates the possibility of using active learning to reduce the costs of data annotation in the context of document image classification, which is one of the core components of modern document processing pipelines. The results of this study demonstrate that by utilizing active learning (AL), deep document classification models can achieve competitive performances to the models trained on fully annotated datasets and, in some cases, even surpass them by annotating only 15–40% of the total training dataset. Furthermore, this study demonstrates that modern AL strategies significantly outperform random querying, and in many cases achieve comparable performance to the models trained on fully annotated datasets even in the presence of practical deployment issues such as data imbalance, and annotation noise, and thus, offer tremendous benefits in real-world deployment of deep document classification models. The code to reproduce our experiments is publicly available at https://github.com/saifullah3396/doc_al.
相似文献Question answer selection in the Chinese medical field is very challenging since it requires effective text representations to capture the complex semantic relationships between Chinese questions and answers. Recent approaches on deep learning, e.g., CNN and RNN, have shown their potential in improving the selection quality. However, these existing methods can only capture a part or one-side of semantic relationships while ignoring the other rich and sophisticated ones, leading to limited performance improvement. In this paper, a series of neural network models are proposed to address Chinese medical question answer selection issue. In order to model the complex relationships between questions and answers, we develop both single and hybrid models with CNN and GRU to combine the merits of different neural network architectures. This is different from existing works that can onpy capture partial relationships by utilizing a single network structure. Extensive experimental results on cMedQA dataset demonstrate that the proposed hybrid models, especially BiGRU-CNN, significantly outperform the state-of-the-art methods. The source codes of our models are available in the GitHub (https://github.com/zhangyuteng/MedicalQA-CNN-BiGRU).
相似文献Music genre classification based on visual representation has been successfully explored over the last years. Recently, there has been increasing interest in attempting convolutional neural networks (CNNs) to achieve the task. However, most of the existing methods employ the mature CNN structures proposed in image recognition without any modification, which results in the learning features that are not adequate for music genre classification. Faced with the challenge of this issue, we fully exploit the low-level information from spectrograms of audio and develop a novel CNN architecture in this paper. The proposed CNN architecture takes the multi-scale time-frequency information into considerations, which transfers more suitable semantic features for the decision-making layer to discriminate the genre of the unknown music clip. The experiments are evaluated on the benchmark datasets including GTZAN, Ballroom, and Extended Ballroom. The experimental results show that the proposed method can achieve 93.9%, 96.7%, 97.2% classification accuracies respectively, which to the best of our knowledge, are the best results on these public datasets so far. It is notable that the trained model by our proposed network possesses tiny size, only 0.18M, which can be applied in mobile phones or other devices with limited computational resources. Codes and model will be available at https://github.com/CaifengLiu/music-genre-classification.
相似文献Person re-identification (Re-ID) in real-world scenarios suffers from various degradations, e.g., low resolution, weak lighting, and bad weather. These degradations hinders identity feature learning and significantly degrades Re-ID performance. To address these issues, in this paper, we propose a degradation invariance learning framework for robust person Re-ID. Concretely, we first design a content-degradation feature disentanglement strategy to capture and isolate task-irrelevant features contained in the degraded image. Then, to avoid the catastrophic forgetting problem, we introduce a memory replay algorithm to further consolidate invariance knowledge learned from the previous pre-training to improve subsequent identity feature learning. In this way, our framework is able to continuously maintain degradation-invariant priors from one or more datasets to improve the robustness of identity features, achieving state-of-the-art Re-ID performance on several challenging real-world benchmarks with a unified model. Furthermore, the proposed framework can be extended to low-level image processing, e.g., low-light image enhancement, demonstrating the potential of our method as a general framework for the various vision tasks. Code and trained models will be available at: https://github.com/hyk1996/Degradation-Invariant-Re-ID-pytorch.
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下载免费PDF全文 High accuracy and fast face alignment algorithms play an important role in many face-related applications. Generally, the model speed is inversely related to the number of parameters. We construct our network based on densely connected encoder-decoders, which is an efficient method to balance the parameter number and localization results. In each encoder-decoder, we introduce stacking depthwise convolution and depthwise feature fusion within the same channel, which greatly improves the performance of depthwise convolution and reduces the number of model parameters. In addition, we enhance the mean square loss function by assigning different penalty weights to each coordinate according to the distance to the position corresponding to the maximum value in the label heatmap. Experiments show that the model with the improved loss function obtains better localization results. In the experiment, we compare our method to state-of-the-art methods based on 300W and WFLW. The localization error is 2.76% with the common subset of 300W and the model size (0.7M) is small and even utilizes approximately 1% of the number of parameters of the other models. The dataset and model based on WFLW are publicly available at https://github.com/iam-zhanghongliang/DC-EDN.
相似文献C-Mantec neural network constructive algorithm Ortega (C-Mantec neural network algorithm implementation on MATLAB. https://github.com/IvanGGomez/CmantecPaco, 2015) creates very compact architectures with generalization capabilities similar to feed-forward networks trained by the well-known back-propagation algorithm. Nevertheless, constructive algorithms suffer much from the problem of overfitting, and thus, in this work the learning procedure is first analyzed for networks created by this algorithm with the aim of trying to understand the training dynamics that will permit optimization possibilities. Secondly, several optimization strategies are analyzed for the position of class separating hyperplanes, and the results analyzed on a set of public domain benchmark data sets. The results indicate that with these modifications a small increase in prediction accuracy of C-Mantec can be obtained but in general this was not better when compared to a standard support vector machine, except in some cases when a mixed strategy is used.
相似文献Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification, e-commerce, media editing, video surveillance, autonomous driving and virtual reality, etc. To perform well, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we first present a new large-scale database “Multi-human Parsing (MHP v2.0)” for algorithm development and evaluation to advance the research on understanding humans in crowded scenes. MHP v2.0 contains 25,403 elaborately annotated images with 58 fine-grained semantic category labels and 16 dense pose key point labels, involving 2–26 persons per image captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background. We further propose a novel deep Nested Adversarial Network (NAN) model for multi-human parsing. NAN consists of three Generative Adversarial Network-like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, including MHP v1.0, PASCAL-Person-Part and Buffy. NAN serves as a strong baseline to shed light on generic instance-level semantic part prediction and drive the future research on multi-human parsing. With the above innovations and contributions, we have organized the CVPR 2018 Workshop on Visual Understanding of Humans in Crowd Scene (VUHCS 2018) and the Fine-Grained Multi-human Parsing and Pose Estimation Challenge. These contributions together significantly benefit the community. Code and pre-trained models are available at https://github.com/ZhaoJ9014/Multi-Human-Parsing_MHP.
相似文献The computational complexity of solving nonlinear support vector machine (SVM) is prohibitive on large-scale data. In particular, this issue becomes very sensitive when the data represents additional difficulties such as highly imbalanced class sizes. Typically, nonlinear kernels produce significantly higher classification quality to linear kernels but introduce extra kernel and model parameters which requires computationally expensive fitting. This increases the quality but also reduces the performance dramatically. We introduce a generalized fast multilevel framework for regular and weighted SVM and discuss several versions of its algorithmic components that lead to a good trade-off between quality and time. Our framework is implemented using PETSc which allows an easy integration with scientific computing tasks. The experimental results demonstrate significant speed up compared to the state-of-the-art nonlinear SVM libraries. Reproducibility: our source code, documentation and parameters are available at https://github.com/esadr/mlsvm.
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