scholarly journals Attention-Guided Image Captioning through Word Information

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7982
Author(s):  
Ziwei Tang ◽  
Yaohua Yi ◽  
Hao Sheng
Keyword(s):  
State Of The Art ◽  
Memory Cell ◽  
Competitive Performance ◽  
Image Description ◽  
Image Captioning ◽  
Research Attention ◽  
Evaluation Score ◽  
Written Descriptions ◽  
Word Attention ◽  
Current Word

Image captioning generates written descriptions of an image. In recent image captioning research, attention regions seldom cover all objects, and generated captions may lack the details of objects and may remain far from reality. In this paper, we propose a word guided attention (WGA) method for image captioning. First, WGA extracts word information using the embedded word and memory cell by applying transformation and multiplication. Then, WGA applies word information to the attention results and obtains the attended feature vectors via elementwise multiplication. Finally, we apply WGA with the words from different time steps to obtain previous word guided attention (PW) and current word attention (CW) in the decoder. Experiments on the MSCOCO dataset show that our proposed WGA can achieve competitive performance against state-of-the-art methods, with PW results of a 39.1 Bilingual Evaluation Understudy score (BLEU-4) and a 127.6 Consensus-Based Image Description Evaluation score (CIDEr-D); and CW results of a 39.1 BLEU-4 score and a 127.2 CIDER-D score on a Karpathy test split.

scholarly journals Text Augmentation Using BERT for Image Captioning

2020 ◽  
Vol 10 (17) ◽  
pp. 5978
Author(s):  
Viktar Atliha ◽  
Dmitrij Šešok
Keyword(s):  
Neural Networks ◽  
Human Computer Interaction ◽  
State Of The Art ◽  
Language Model ◽  
Research Field ◽  
The State ◽  
Training Dataset ◽  
Image Description ◽  
Image Captioning ◽  
Training Set

Image captioning is an important task for improving human-computer interaction as well as for a deeper understanding of the mechanisms underlying the image description by human. In recent years, this research field has rapidly developed and a number of impressive results have been achieved. The typical models are based on a neural networks, including convolutional ones for encoding images and recurrent ones for decoding them into text. More than that, attention mechanism and transformers are actively used for boosting performance. However, even the best models have a limit in their quality with a lack of data. In order to generate a variety of descriptions of objects in different situations you need a large training set. The current commonly used datasets although rather large in terms of number of images are quite small in terms of the number of different captions per one image. We expanded the training dataset using text augmentation methods. Methods include augmentation with synonyms as a baseline and the state-of-the-art language model called Bidirectional Encoder Representations from Transformers (BERT). As a result, models that were trained on a datasets augmented show better results than that models trained on a dataset without augmentation.

scholarly journals Panoptic Segmentation-Based Attention for Image Captioning

2020 ◽  
Vol 10 (1) ◽  
pp. 391
Author(s):  
Wenjie Cai ◽  
Zheng Xiong ◽  
Xianfang Sun ◽  
Paul L. Rosin ◽  
Longcun Jin ◽  
...  
Keyword(s):  
Main Part ◽  
State Of The Art ◽  
Image Representation ◽  
Experimental Results ◽  
Competitive Performance ◽  
Image Captioning ◽  
Feature Vectors ◽  
Fine Grained ◽  
Art Methods

Image captioning is the task of generating textual descriptions of images. In order to obtain a better image representation, attention mechanisms have been widely adopted in image captioning. However, in existing models with detection-based attention, the rectangular attention regions are not fine-grained, as they contain irrelevant regions (e.g., background or overlapped regions) around the object, making the model generate inaccurate captions. To address this issue, we propose panoptic segmentation-based attention that performs attention at a mask-level (i.e., the shape of the main part of an instance). Our approach extracts feature vectors from the corresponding segmentation regions, which is more fine-grained than current attention mechanisms. Moreover, in order to process features of different classes independently, we propose a dual-attention module which is generic and can be applied to other frameworks. Experimental results showed that our model could recognize the overlapped objects and understand the scene better. Our approach achieved competitive performance against state-of-the-art methods. We made our code available.

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

scholarly journals Single-Electron Memories

VLSI Design ◽  
1998 ◽  
Vol 8 (1-4) ◽  
pp. 219-223 ◽  
Author(s):  
Christoph Wasshuber ◽  
Hans Kosina ◽  
Siegfried Selberherr
Keyword(s):  
Power Consumption ◽  
Room Temperature ◽  
State Of The Art ◽  
Memory Cell ◽  
Single Electron ◽  
Lower Power ◽  
Memory Chip ◽  
Single Electronics ◽  
Background Charge ◽  
Random Background

One of the most promising applications of single-electronics is a single-electron memory chip. Such a chip would have orders of magnitude lower power consumption compared to state-of-the-art dynamic memories, and would allow integration densities beyond the tera bit chip.We studied various single-electron memory designs. Additionally we are proposing a new memory cell which we call the T-memory cell. This cell can be manufactured with state-of-the-art lithography, it operates at room temperature and shows a strong resistance against random background charge.

scholarly journals Densely Supervised Hierarchical Policy-Value Network for Image Paragraph Generation

2019 ◽  
Author(s):  
Siying Wu ◽  
Zheng-Jun Zha ◽  
Zilei Wang ◽  
Houqiang Li ◽  
Feng Wu
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Cross Entropy ◽  
Image Captioning ◽  
Value Network ◽  
Entropy Loss ◽  
Fine Grained ◽  
Performance Improvements ◽  
Single Sentence ◽  
Multiple State

Image paragraph generation aims to describe an image with a paragraph in natural language. Compared to image captioning with a single sentence, paragraph generation provides more expressive and fine-grained description for storytelling. Existing approaches mainly optimize paragraph generator towards minimizing word-wise cross entropy loss, which neglects linguistic hierarchy of paragraph and results in ``sparse" supervision for generator learning. In this paper, we propose a novel Densely Supervised Hierarchical Policy-Value (DHPV) network for effective paragraph generation. We design new hierarchical supervisions consisting of hierarchical rewards and values at both sentence and word levels. The joint exploration of hierarchical rewards and values provides dense supervision cues for learning effective paragraph generator. We propose a new hierarchical policy-value architecture which exploits compositionality at token-to-token and sentence-to-sentence levels simultaneously and can preserve the semantic and syntactic constituent integrity. Extensive experiments on the Stanford image-paragraph benchmark have demonstrated the effectiveness of the proposed DHPV approach with performance improvements over multiple state-of-the-art methods.

scholarly journals Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation

2019 ◽  
Vol 33 ◽  
pp. 6626-6633
Author(s):  
Xiang Kong ◽  
Qizhe Xie ◽  
Zihang Dai ◽  
Eduard Hovy
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
The State ◽  
Computational Time ◽  
Memory Consumption ◽  
Image Captioning ◽  
Vocabulary Size ◽  
Language Generation ◽  
Practical Applications ◽  
Coding Schemes

Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes. In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden. We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses. With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on the larger WMT 2014 machine translation dataset, our MoSboosted Transformer yields 29.6 BLEU score for English-toGerman and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.9 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task.

scholarly journals Unified Vision-Language Pre-Training for Image Captioning and VQA

2020 ◽  
Vol 34 (07) ◽  
pp. 13041-13049 ◽  
Author(s):  
Luowei Zhou ◽  
Hamid Palangi ◽  
Lei Zhang ◽  
Houdong Hu ◽  
Jason Corso ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Question Answering ◽  
State Of The Art ◽  
Learning Objectives ◽  
Image Captioning ◽  
Language Generation ◽  
Visual Question Answering ◽  
Benchmark Datasets

This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering) tasks, and (2) it uses a shared multi-layer transformer network for both encoding and decoding, which differs from many existing methods where the encoder and decoder are implemented using separate models. The unified VLP model is pre-trained on a large amount of image-text pairs using the unsupervised learning objectives of two tasks: bidirectional and sequence-to-sequence (seq2seq) masked vision-language prediction. The two tasks differ solely in what context the prediction conditions on. This is controlled by utilizing specific self-attention masks for the shared transformer network. To the best of our knowledge, VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions, and VQA 2.0. The code and the pre-trained models are available at https://github.com/LuoweiZhou/VLP.

scholarly journals Middle-Level Attribute-Based Language Retouching for Image Caption Generation

2018 ◽  
Vol 8 (10) ◽  
pp. 1850 ◽  
Author(s):  
Zhibin Guan ◽  
Kang Liu ◽  
Yan Ma ◽  
Xu Qian ◽  
Tongkai Ji
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Middle Level ◽  
Generation Model ◽  
Image Description ◽  
Image Captioning ◽  
Benchmark Datasets ◽  
Intermediate Image ◽  
Image Caption Generation ◽  
Image Caption

Image caption generation is attractive research which focuses on generating natural language sentences to describe the visual content of a given image. It is an interdisciplinary subject combining computer vision (CV) and natural language processing (NLP). The existing image captioning methods are mainly focused on generating the final image caption directly, which may lose significant identification information of objects contained in the raw image. Therefore, we propose a new middle-level attribute-based language retouching (MLALR) method to solve this problem. Our proposed MLALR method uses the middle-level attributes predicted from the object regions to retouch the intermediate image description, which is generated by our language generation model. The advantage of our MLALR method is that it can correct descriptive errors in the intermediate image description and make the final image caption more accurate. Moreover, evaluation using benchmark datasets—MSCOCO, Flickr8K, and Flickr30K—validated the impressive performance of our MLALR method with evaluation metrics—BLEU, METEOR, ROUGE-L, CIDEr, and SPICE.

scholarly journals Task-Aware Monocular Depth Estimation for 3D Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12257-12264 ◽  
Author(s):  
Xinlong Wang ◽  
Wei Yin ◽  
Tao Kong ◽  
Yuning Jiang ◽  
Lei Li ◽  
...  
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Depth Estimation ◽  
3D Perception ◽  
Research Attention ◽  
3D Object ◽  
Depth Prediction ◽  
Monocular Depth ◽  
Almost All ◽  
3D Object Detection

Monocular depth estimation enables 3D perception from a single 2D image, thus attracting much research attention for years. Almost all methods treat foreground and background regions (“things and stuff”) in an image equally. However, not all pixels are equal. Depth of foreground objects plays a crucial role in 3D object recognition and localization. To date how to boost the depth prediction accuracy of foreground objects is rarely discussed. In this paper, we first analyze the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground and background depth using separate optimization objectives and decoders. Our method significantly improves the depth estimation performance on foreground objects. Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new state-of-the-art results among other monocular methods. Code will be available at: https://github.com/WXinlong/ForeSeE.

scholarly journals Towards Explanatory Interactive Image Captioning Using Top-Down and Bottom-Up Features, Beam Search and Re-ranking

2020 ◽  
Vol 34 (4) ◽  
pp. 571-584
Author(s):  
Rajarshi Biswas ◽  
Michael Barz ◽  
Daniel Sonntag
Keyword(s):  
State Of The Art ◽  
Input Image ◽  
The State ◽  
Beam Search ◽  
Image Captioning ◽  
Bottom Up ◽  
Interactive Machine Learning ◽  
Joint Embedding ◽  
Bounding Boxes ◽  
High Level

AbstractImage captioning is a challenging multimodal task. Significant improvements could be obtained by deep learning. Yet, captions generated by humans are still considered better, which makes it an interesting application for interactive machine learning and explainable artificial intelligence methods. In this work, we aim at improving the performance and explainability of the state-of-the-art method Show, Attend and Tell by augmenting their attention mechanism using additional bottom-up features. We compute visual attention on the joint embedding space formed by the union of high-level features and the low-level features obtained from the object specific salient regions of the input image. We embed the content of bounding boxes from a pre-trained Mask R-CNN model. This delivers state-of-the-art performance, while it provides explanatory features. Further, we discuss how interactive model improvement can be realized through re-ranking caption candidates using beam search decoders and explanatory features. We show that interactive re-ranking of beam search candidates has the potential to outperform the state-of-the-art in image captioning.

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