Author Profiling in Code-Mixed WhatsApp Messages Using Stacked Convolution Networks and Contextualized Embedding Based Text Augmentation

Devi, V. Sharmila; Kannimuthu, S.

doi:10.1007/s11063-022-10898-3

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…Arunkumar et al [26] adopted support vector machine and other machine learning methods to improve the accuracy of emotional evaluation of medical videos. Devi et al [27] used stacked convolutional neural network to analyze the authors of WHATSAPP accurately. Raja et al [28] proposed a new conditional generation network C-GAN, which improved the recognition accuracy of autism spectrum disorders.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

Zhou,

Huang,

Sun

2023

CMC

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The remote sensing ships' fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For the complex background of remote sensing images, the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model's feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets. For the problem of multi-grained feature mixing in remote sensing images, the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features. The parallel network has different depths, considering both high-level and lowlevel semantic information. The model achieves better classification performance in remote sensing ship images with multi-granularity mixing. Finally, the model achieves 88.73% and 94.56% accuracy on the public fine-grained ship collection-23 (FGSC-23) and FGSCR-42 datasets, respectively, while the parameter size is only 53.47 M, the floating point operations is 9.9 G. The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

Zhou,

Huang,

Sun

2023

CMC

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“…Sun et al [7] proposed hierarchical attention to learn text representations via linguistic features, and Liang et al [8] introduced a GCN approach to distinguish target-specific and invariant features. Furthermore, Devi and Kannimuthu [9] incorporated focal-loss and context-embedding-based data augmentation to handle the data imbalance. Inspired by promising PLM results, fine-tuning strategies have been developed to enhance the accuracy of stance detection [10].…”

Section: Introductionmentioning

confidence: 99%

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

Ding,

Dai,

Peng

et al. 2024

Mathematics

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Investigating public attitudes on social media is crucial for opinion mining systems. Stance detection aims to predict the attitude towards a specific target expressed in a text. However, effective neural stance detectors require substantial training data, which are challenging to curate due to the dynamic nature of social media. Moreover, deep neural networks (DNNs) lack explainability, rendering them unsuitable for scenarios requiring explanations. We propose a distantly supervised explainable stance detection framework (DS-ESD), comprising an instruction-based chain-of-thought (CoT) method, a generative network, and a transformer-based stance predictor. The CoT method employs prompt templates to extract stance detection explanations from a very large language model (VLLM). The generative network learns the input-explanation mapping, and a transformer-based stance classifier is trained with VLLM-annotated stance labels, implementing distant supervision. We propose a label rectification strategy to mitigate the impact of erroneous labels. Experiments on three benchmark datasets showed that our model outperformed the compared methods, validating its efficacy in stance detection tasks. This research contributes to the advancement of explainable stance detection frameworks, leveraging distant supervision and label rectification strategies to enhance performance and interpretability.

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“…Researchers used different types of approaches for image colorization. Existing methods for coloring gray images mainly fall into two categories: user-guided colorization [1][2][3] and data-driven colorization [4][5][6][7][8][9][10]. Traditional user-guided colorization requires too much human interaction for coloring the image perfectly [4,5,9].…”

Section: Introductionmentioning

confidence: 99%

“…The user sets color values manually based on the reference image. Nowadays, Convolutional Neural Network (CNN)-based deep learning methods show tremendous progress in various fields like Natural Language Processing (NLP) [10] and image processing [11]. In the field of image colorization, CNN is also getting popularity.…”

Section: Introductionmentioning

confidence: 99%

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

Gain¹,

Rahman²,

Debnath³

et al. 2023

Computer Systems Science and Engineering

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Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated color components compared to saturated color components in the training images, the saturated color components have a strong tendency towards desaturated color components in the predicted a * b * channel. To solve the problems, we rebalance the predicted a * b * color channel by smoothing every subregion individually using the average filter. 2 stage k-means clustering technique is applied to divide the subregions. Then we apply Gamma transformation in the entire a * b * channel to saturate the image. We compare our proposed method with several existing methods. From the experimental results, we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to visualize. Additionally, our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Histogram.

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Author Profiling in Code-Mixed WhatsApp Messages Using Stacked Convolution Networks and Contextualized Embedding Based Text Augmentation

Cited by 12 publications

References 25 publications

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

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