VD-SAN: Visual-Densely Semantic Attention Network for Image Caption Generation

Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that contain textual information. These datasets are not sufficient for the proposed model and this has resulted in the creation of a new image caption dataset that contains images with textual content. With the newly created dataset, comparative analysis of the experimental results is performed on the proposed model and the existing pre-trained model. The obtained experimental results show that the proposed model is equally effective as the existing one in subtitle image captioning models and provides more insights about the image by performing text extraction.

Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available.

Unsupervised image captioning often grapples with challenges such as image–text mismatches and modality gaps, resulting in suboptimal captions. This paper introduces a semantic-enhanced cross-modal fusion model (SCFM) to address these issues. The SCFM integrates three innovative components: a text semantic enhancement network (TSE-Net) for nuanced semantic representation; contrast learning for optimizing similarity measures between text and images; and enhanced visual selection decoding (EVSD) for precise captioning. Unlike existing methods that struggle with capturing accurate semantic relationships and flexibility across scenarios, the proposed model provides a robust solution for unbiased and diverse captioning. Through experimental evaluations on the MS COCO and Flickr30k datasets, SCFM demonstrates significant improvements over the benchmark model, enhancing the CIDEr and BLEU-4 metrics by 3.6% and 3.2%, respectively. Visualization analysis further reveals the model’s superiority in increasing variability between hidden features and its potential in cross-domain and stylized image captioning. The findings not only contribute to the advancement of image captioning techniques but also open avenues for future research. Further investigations will explore SCFM’s adaptability to other multimodal tasks and refine it for more intricate image–text relationships.

With the success of convolutional neural network (CNN) in the field of image processing and image recognition, an improved fully convolutional neural network (FCNN) based on CNN is proposed. Although FCNN has better performance than CNN, the number of parameters of FCNN has also increased significantly compared to CNN. Consequently, this paper proposes a multi‐feature fusion decision convolutional neural network (MFFD‐CNN), which use two kinds down‐sample methods in the pooling layer, and chooses the concatenate or add operation as the way that consolidates the feature of the convolutional layer with a stride of 2 and the max‐pooling layer to execute fusion operations. Meanwhile replaces the traditional softmax loss with an additive margin Softmax (AM‐Softmax) loss. The structure, which is based on the multi‐target classification experiment results of the MSTAR data set, shows can not only obtain an average correct recognition rate 1.2% higher than FCNN, but also better stability without augmenting the training samples of MSTAR data.

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models.

Generating image captions is a very important task in the field of AI. The rise of deep learning methods has driven the development of advanced models that merge image feature extraction with natural language processing, enabling systems to generate captions that are both coherent and meaningful. Image captioning requires identifying key objects, understanding their relationships, and creating a description that flows naturally, with proper grammar. Our approach uses a convolutional neural network (CNN) for extracting visual features and a long short-term memory (LSTM) network for generating natural language captions. The model is trained and evaluated on the Flickr8k dataset. Additionally, beam search and T5 refinement have been included to enhance fluency and coherence.

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities.

This research study is to detect the Epileptic seizures using Novel Multi-Layered Convolutional Neural Networks (NMLCNN) and to compare the performance with Fully Convolutional Neural Networks (FCNN). By using Novel Multi-Layered Convolution Neural Networks algorithm works with the data collected from the observations of nervous system cells. The changes in the Epileptic seizures from the resource’s constraint, when the criteria met with randomizing, this will arrange in random ways for n times. The most typical beginning of a seizure is detectable through analysis of the Electroencephalogram (EEG). To examine this, the study utilized 20 samples in each group, with a Gpower value of 80%. The study used a predefined significance value and a Confidence Interval (CI) of 95%.This experiment found that NMLCNN has 92.25% and FCNN has 87.55% of accuracy for epileptic seizure. The Novel Multi-Layered Convolutional Neural Network achieved 0.012 significance using group analysis and provided significance. This probing concludes based on accuracy and significance results, detection of Epileptic seizures using Novel Multi-layered Convolutional Neural Networks has performed better compared to Fully Convolutional Neural Network.

With the leaps and bounds of computer performance and the advent of the era of big data, deep learning has drawn more and more attention from all walks of life. It can combine low-level features to form more abstract high-level features and describe the data more essentially. Therefore, it is widely used in various fields such as computer vision. Image segmentation is one of the most basic research topics in the field of computer vision. The main purpose is to extract regions of interest from images for later image processing research. In 3D reconstruction based on sequence images, the segmentation accuracy and speed of sequence images determine the quality and efficiency of target reconstruction. Therefore, when facing large-scale sequence images, the biggest problem is how to improve the segmentation speed while ensuring accuracy. Based on the above background, the research content of this article is an indoor scene segmentation algorithm based on full convolutional neural network. According to the characteristics of indoor application scenes, this paper proposes a fast convolutional neural network image segmentation method to segment the indoor scene image and construct the fast fully convolutional networks (FFCN) for indoor scene image segmentation uses inter-layer fusion to reduce the amount of network calculation parameters and avoid the loss of picture feature information by continuous convolution. In order to verify the effectiveness of the network, in this paper, a basic living object data set (XAUT data set) in an indoor environment is created. The XAUT data set is used to train the FFCN network under the Caffe framework to obtain an indoor scene segmentation model. In order to compare the effectiveness of the model, the structure of the worn FCN8s, FCN16s, and FCN32s models was fine-tuned, and the corresponding algorithm model for indoor scene segmentation was obtained by training with the XAUT data set. The experimental results show that the pixel recognition accuracy of all types of networks has reached 86%, and the mean IU ratio has reached more than 63%. The mean IU of the FCN8s network is the highest at 70.38%, but its segmentation speed is only 1/5 of FFCN. On the premise that other types of indicators are not much different, the average segmentation speed on FFCN fast segmentation convolutional neural network reaches 40 fps. It can be seen that the scale fusion technology can well avoid the loss of image feature information in the network convolution and reddening process. Compared with other FCN networks, it has a faster speed and is conducive to real-time image preprocessing.

Unlike standard Convolutional Neural Networks (CNNs) with fully-connected layers, Fully Convolutional Neural Networks (FCN) are prevalent in computer vision applications such as object detection, semantic/image segmentation, and the most popular generative tasks based on Generative Adversarial Networks (GAN). In an FCN, traditional convolutional layers and deconvolutional layers contribute to the majority of the computation complexity. However, prior deep learning accelerator designs mostly focus on CNN optimization. They either use independent compute-resources to handle deconvolution or convert deconvolutional layers (Deconv) into general convolution operations, which arouses considerable overhead. To address this problem, we propose a unified fully convolutional accelerator aiming to handle both the deconvolutional and convolutional layers with a single processing element (PE) array. We re-optimize the conventional CNN accelerator architecture of regular 2D processing elements array, to enable it more efficiently support the data flow of deconvolutional layer inference. By exploiting the locality in deconvolutional filters, this architecture reduces the consumption of on-chip memory communication from 24.79 GB to 6.56 GB and improves the power efficiency significantly. Compared to prior baseline deconvolution acceleration scheme, the proposed accelerator achieves 1.3X -- 44.9X speedup and reduces the energy consumption by 14.6%-97.6% on a set of representative benchmark applications. Meanwhile, it keeps similar CNN inference performance to that of an optimized CNN-only accelerator with negligible power consumption and chip area overhead.

Both accuracy and efficiency are crucial for image captioning in real-world scenarios. Although Transformer-based models have gained significant improved captioning performance, their computational cost is very high. A feasible way to reduce the time complexity is to exit the prediction early in internal decoding layers without passing the entire model. However, it is not straightforward to devise early exiting into image captioning due to the following issues. On one hand, the representation in shallow layers lacks high-level semantic and sufficient cross-modal fusion information for accurate prediction. On the other hand, the exiting decisions made by internal classifiers are unreliable sometimes. To solve these issues, we propose DeeCap framework for efficient image captioning, which dynamically selects proper-sized decoding layers from a global perspective to exit early. The key to successful early exiting lies in the specially designed imitation learning mechanism, which predicts the deep layer activation with shallow layer features. By deliberately merging the imitation learning into the whole image captioning architecture, the imitated deep layer representation can mitigate the loss brought by the missing of actual deep layers when early exiting is undertaken, resulting in significant reduction in calculation cost with small sacrifice of accuracy. Experiments on the MS COCO and Flickr30k datasets demonstrate the DeeCap can achieve competitive performances with 4× speed-up. Code is available at: https://github.com/feizc/DeeCap.

With the surge in technological innovations and advancements, data is considered to be the most important commodity in today’s world. The collection and dissemination of massive volumes of data have swamped our world today. This has given rise to problems in domains like searching, indexing, and value extraction. Hence image captioning rises as a solution to all these problems. The literature survey revealed that there is a lot of fog around which architectures work best for feature extraction in image captioning. To tackle this gap we’ve proposed a centralized analysis on which algorithms give the best results in terms of image captioning in this paper. We’ve taken algorithms like Inception, ResNet, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Visual Geometry Group 16 (VGG16), etc. into account for our system. In this analysis, we take into consideration all the important aspects that affect image captioning to maximize the results at the end. Dataset taken into consideration is Flickr 8k. Hence the fundamentals of the approaches are discussed in this paper to examine their performances, strengths, and constraints.

In the field of 3D Human Pose Estimation and Reconstruction based on body joints extracted from a 2D image; Exists challenges like self-occlusion and depth perception. These problems hinder approximate estimations. This article proposes a hybrid method that consists of semantic segmentation, sparse representation and 2D pose estimation for 3D estimation. Specifically, we train two fully Convolution neural networks (FCNs) to estimate 2D pose and semantic segmentation. Then, we narrow the estimation down using basic human body structure and results from the FCNs. Subsequently, utilizing the estimated 2D pose of the earlier step and a sparse representation model the 3D pose will estimate. Using a Convolution Neural Networks learning method for 2D human pose estimation and then by sparse representation will estimate. Results of the proposed method, show improvement relative to the previous works on 3D Human pose estimation. 3D Human Pose Estimation by this method demonstrates a significant mean error reduction compared to earlier studies.

Automated identification and localization of vertebrae in spinal computed tomography (CT) imaging is a complicated hybrid task. This task requires detecting and indexing a long sequence in a 3-D image, and both image feature extraction and sequence modeling are needed to address the problem. In this paper, the powerful fully convolutional neural network (FCN) technique performs both of these tasks simultaneously because FCNs directly encode and decode the spatial interdependence of different components in images. The key module of our proposed framework is a 3-D FCN trained in an end-to-end manner at the spine level to capture the long-range contextual information in CT volumes. The large increase in the calculation due to the full-size image inputs is alleviated by the scale-down of the inputs and the use of an auxiliary FCN to compensate for the loss of details. The composite network pipeline design enables the integration of local image details and global image patterns. Furthermore, explicit spatial and sequential constraints are imposed by the hidden Markov model (HMM) for a higher robustness and a clearer interpretation of network outputs. The proposed framework is quantitatively evaluated on the public dataset from the MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification and demonstrates an identification rate (within 20 mm) of 94.67%, a mean identification rate of 87.97%, and a mean error distance of 2.56 mm on the test set, thus achieving the highest performance reported on this dataset.

In recent years, convolutional neural networks (CNNs) have been successfully applied to inverse synthetic aperture radar (ISAR) sparse imaging because of their powerful ability in feature extraction. However, these CNNs only adopt single path feed-forward architectures and lack paths for directly transmitting original feature representations (OFRs) in shallow layers to reconstruction layers, which limits the complete reconstruction of target shape due to the underutilization of the OFRs that are efficient for recovering target details. Later, fully CNN (FCNN) introduces several skip connections (SKs) to establish the additional ways for directly passing the OFRs to the reconstruction layers. Nevertheless, the transmitted OFRs inevitably include the feature information of artifacts, which usually results the appearance of artifacts in final reconstructed target image. To address this issue, we introduce the gate units to FCNN, and refer to the improved FCNN as G-FCNN. Furthermore, the learnable gate units weight the OFRs transmitted by SKs and autonomously decide how many OFRs are transmitted further. To circumvent the shortage of the real data available for network training, we utilize the transfer learning strategy to guarantee a good performance of the G-FCNN. The imaging results of real data show that the G-FCNN-based imaging method is superior to the existing CNN-based imaging methods.