This paper studies neural machine translation (NMT) of code-mixed (CM) text. Specifically, we generate synthetic CM data and how it can be used to improve the translation performance of NMT through the data augmentati...
详细信息
This paper studies neural machine translation (NMT) of code-mixed (CM) text. Specifically, we generate synthetic CM data and how it can be used to improve the translation performance of NMT through the data augmentation strategy. We conduct experiments on three data augmentation approaches viz. CM-Augmentation, CM-Concatenation, and multi-encoder approaches, and the latter two approaches are inspired by document-level NMT, where we use synthetic CM data as context to improve the performance of the NMT models. We conduct experiments on three language pairs, viz. Hindi-English, Telugu-English and Czech-English. Experimental results demonstrate that the proposed approaches significantly improve performance over the baseline model trained without data augmentation and over the existing data augmentation strategies. The CM-Concatenation model attains the best performance.
Video frame interpolation synthesises a new frame from existing frames. Several approaches have been devised to handle this core computer vision problem. Kernel-based approaches use an encoder-decoder architecture to ...
详细信息
Video frame interpolation synthesises a new frame from existing frames. Several approaches have been devised to handle this core computer vision problem. Kernel-based approaches use an encoder-decoder architecture to extract features from the inputs and generate weights for a local separable convolution operation which is used to warp the input frames. The warped inputs are then combined to obtain the final interpolated frame. The ease of implementation of such an approach and favourable performance have enabled it to become a popular method in the field of interpolation. One downside, however, is that the encoder-decoder feature extractor is large and uses a lot of parameters. We propose a multi-encoder Method for Parameter Reduction (MEMPR) that can significantly reduce parameters by up to 85% whilst maintaining a similar level of performance. This is achieved by leveraging multiple encoders to focus on different aspects of the input. The approach can also be used to improve the performance of kernel-based models in a parameter-effective manner. To encourage the adoption of such an approach in potential future kernel-based methods, the approach is designed to be modular, intuitive and easy to implement. It is implemented on some of the most impactful kernel-based works such as SepConvNet, AdaCoFNet and EDSC. Extensive experiments on datasets with varying ranges of motion highlight the effectiveness of the MEMPR approach and its generalisability to different convolutional backbones and kernel-based operators.
In recent years, hardware advancements have enabled natural language processing tasks that were previously difficult to achieve due to their intense computing requirements. This study focuses on paraphrase generation,...
详细信息
In recent years, hardware advancements have enabled natural language processing tasks that were previously difficult to achieve due to their intense computing requirements. This study focuses on paraphrase generation, which entails rewriting a sentence using different words and sentence structures while preserving its original meaning. This increases sentence diversity, thereby improving the performance of downstream tasks, such as question-answering systems and machine translation. This study proposes a novel paraphrase generation model that combines the Transformer architecture with part-of-speech features, and this model is trained using a Chinese corpus. New features are incorporated to improve the performance of the Transformer architecture, and the pointer generation network is used when the training data contain low-frequency words. This allows the model to focus on input words with important information according to their attention distributions.
Influenced by its training corpus,the performance of different machine translation systems varies *** at achieving higher quality translations,system combination methods combine the translation results of multiple sys...
详细信息
Influenced by its training corpus,the performance of different machine translation systems varies *** at achieving higher quality translations,system combination methods combine the translation results of multiple systems through statistical combination or neural network *** paper proposes a new multi-system translation combination method based on the Transformer architecture,which uses a multi-encoder to encode source sentences and the translation results of each system in order to realize encoder combination and decoder *** experimental verification on the Chinese-English translation task shows that this method has 1.2-2.35 more bilingual evaluation understudy(BLEU)points compared with the best single system results,0.71-3.12 more BLEU points compared with the statistical combination method,and 0.14-0.62 more BLEU points compared with the state-of-the-art neural network combination *** experimental results demonstrate the effectiveness of the proposed system combination method based on Transformer.
Glioma, a primary tumor derived from brain glial cells, is around 45% of all intracranial tumors. Magnetic resonance imaging's (MRI's) precise glioma segmentation is crucial for clinical purposes. This article...
详细信息
Glioma, a primary tumor derived from brain glial cells, is around 45% of all intracranial tumors. Magnetic resonance imaging's (MRI's) precise glioma segmentation is crucial for clinical purposes. This article presents a novel automatic brain tumor segmentation approach based on a multi-encoder-based federated intelligent deep learning framework. The suggested method uses a U-shaped network design that multiplies the single contraction path into several paths to explore semantic information modalities deeply. The basic convolutional layer uses an Inception module and dilated convolutions to extract multi-scale features from the images using artificial intelligent. To emphasize segmentation-related information while ignoring redundant channel dimension information and improving the accuracy of network segmentation, lightweight channel attention efficient channel attention (ECA) modules are inserted into the bottleneck layer and decoder. The collection of data for the 2018 Brain Tumor Segmentation Challenge (BraTS 2018) is used to test the effectiveness of the suggested structure, and the findings indicate that the growth core, for the entire tumor and the augmented tumor regions, respectively, the average Dice coefficients are 0.880, 0.784, and 0.757. These findings support the proposed algorithm's ability to accurately and successfully segregate multimodal MRI brain tumors.
Background and objective: Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learnin...
详细信息
Background and objective: Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long-range relationships between voxels cannot be captured due to the localized character of the convolution procedure. Method: Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context-aware module was introduced to capture the long-range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance. Result: Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state-of-the-art methods. Conclusion: The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.
Recent studies show a significant growth in semantic segmentation. However, many semantic segmen-tation models still have a large number of parameters, making them unsuitable for resource-constrained embedded devices....
详细信息
Recent studies show a significant growth in semantic segmentation. However, many semantic segmen-tation models still have a large number of parameters, making them unsuitable for resource-constrained embedded devices. To address this issue, we propose an efficient Shared Feature Reuse Segmentation (SFRSeg) model containing several novelties: a new yet effective shared-branch multiple sub-encoders design, a context mining module and a semantic aggregating module for better context granularity. In particular, our shared-branch approach improves the entire feature hierarchy by sharing the spatial and context knowledge in both shallow and deep branches. After every shared point in each sub-encoder, a proposed cascading context mining (CCM) module is deployed to filter out the noisy spatial details from the feature maps and provides a diverse size of receptive fields for capturing the latent context between multi-scale geometric shapes in the scene. To overcome the gradient vanishing issue at the early stage, we reduce the number of layers in the first sub-encoder and employ a unique multiple sub-encoders design which reprocesses the rich global feature maps through multiple sub-encoders for better feature refinement. Later, the rich semantic features generated by the efficient sub-encoders at different levels are fused by the proposed Hybrid Path Attention Semantic Aggregation (HPA-SA) module that effectively reduces the semantic gap between feature maps at different levels and alleviate the well-known bound-ary degeneration effect. To make it computationally efficient for resource-constrained embedded devices, a series of lightweight methods such as a lightweight encoder, a squeeze-and-excitation design, separa-ble convolution filters, channel reduction (CR) are carefully exploited. With an exceptional performance on Cityscapes (70.6% test mIoU) and CamVid (74.7% test mIoU) data sets, the proposed model is shown to be superior over existing light real-time semantic seg
暂无评论