scholarly journals Localizing Natural Language in Videos

2019 ◽  
Vol 33 ◽  
pp. 8175-8182 ◽  
Author(s):  
Jingyuan Chen ◽  
Lin Ma ◽  
Xinpeng Chen ◽  
Zequn Jie ◽  
Jiebo Luo
Keyword(s):  
Natural Language ◽  
Video Sequence ◽  
State Of The Art ◽  
Recurrent Networks ◽  
The Public ◽  
Fine Grained ◽  
Proposed Model ◽  
Boundary Model ◽  
The Given ◽  
Language Description

In this paper, we consider the task of natural language video localization (NLVL): given an untrimmed video and a natural language description, the goal is to localize a segment in the video which semantically corresponds to the given natural language description. We propose a localizing network (LNet), working in an end-to-end fashion, to tackle the NLVL task. We first match the natural sentence and video sequence by cross-gated attended recurrent networks to exploit their fine-grained interactions and generate a sentence-aware video representation. A self interactor is proposed to perform crossframe matching, which dynamically encodes and aggregates the matching evidences. Finally, a boundary model is proposed to locate the positions of video segments corresponding to the natural sentence description by predicting the starting and ending points of the segment. Extensive experiments conducted on the public TACoS and DiDeMo datasets demonstrate that our proposed model performs effectively and efficiently against the state-of-the-art approaches.

A Hindi Image Caption Generation Framework Using Deep Learning

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Santosh Kumar Mishra ◽  
Rijul Dhir ◽  
Sriparna Saha ◽  
Pushpak Bhattacharyya
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Image Captioning ◽  
Textual Description ◽  
Proposed Model ◽  
Hindi Language ◽  
The Given

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning for the English language. In this article, we have developed a model for image captioning in the Hindi language. Hindi is the official language of India, and it is the fourth most spoken language in the world, spoken in India and South Asia. To the best of our knowledge, this is the first attempt to generate image captions in the Hindi language. A dataset is manually created by translating well known MSCOCO dataset from English to Hindi. Finally, different types of attention-based architectures are developed for image captioning in the Hindi language. These attention mechanisms are new for the Hindi language, as those have never been used for the Hindi language. The obtained results of the proposed model are compared with several baselines in terms of BLEU scores, and the results show that our model performs better than others. Manual evaluation of the obtained captions in terms of adequacy and fluency also reveals the effectiveness of our proposed approach. Availability of resources : The codes of the article are available at https://github.com/santosh1821cs03/Image_Captioning_Hindi_Language ; The dataset will be made available: http://www.iitp.ac.in/∼ai-nlp-ml/resources.html .

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 Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Canghong Jin ◽  
Zhiwei Lin ◽  
Minghui Wu
Keyword(s):  
State Of The Art ◽  
Real Life ◽  
Traffic Planning ◽  
Trajectory Prediction ◽  
User Intention ◽  
Convolutional Networks ◽  
Proposed Model ◽  
Gated Recurrent Units ◽  
The Given ◽  
Travel Recommendation

Human trajectory prediction is an essential task for various applications such as travel recommendation, location-sensitive advertisement, and traffic planning. Most existing approaches are sequential-model based and produce a prediction by mining behavior patterns. However, the effectiveness of pattern-based methods is not as good as expected in real-life conditions, such as data sparse or data missing. Moreover, due to the technical limitations of sensors or the traffic situation at the given time, people going to the same place may produce different trajectories. Even for people traveling along the same route, the observed transit records are not exactly the same. Therefore trajectories are always diverse, and extracting user intention from trajectories is difficult. In this paper, we propose an augmented-intention recurrent neural network (AI-RNN) model to predict locations in diverse trajectories. We first propose three strategies to generate graph structures to demonstrate travel context and then leverage graph convolutional networks to augment user travel intentions under graph view. Finally, we use gated recurrent units with augmented node vectors to predict human trajectories. We experiment with two representative real-life datasets and evaluate the performance of the proposed model by comparing its results with those of other state-of-the-art models. The results demonstrate that the AI-RNN model outperforms other methods in terms of top-k accuracy, especially in scenarios with low similarity.

scholarly journals Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering

2019 ◽  
Vol 33 ◽  
pp. 8658-8665 ◽  
Author(s):  
Xiangpeng Li ◽  
Jingkuan Song ◽  
Lianli Gao ◽  
Xianglong Liu ◽  
Wenbing Huang ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Computation Time ◽  
Comparable Result ◽  
Video Encoding ◽  
Visual Question Answering ◽  
Proposed Model ◽  
Ablation Study ◽  
The Given ◽  
Video Question Answering

Most of the recent progresses on visual question answering are based on recurrent neural networks (RNNs) with attention. Despite the success, these models are often timeconsuming and having difficulties in modeling long range dependencies due to the sequential nature of RNNs. We propose a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering. Specifically, inspired by the success of self-attention in machine translation task, we propose a Positional Self-Attention to calculate the response at each position by attending to all positions within the same sequence, and then add representations of absolute positions. Therefore, PSAC can exploit the global dependencies of question and temporal information in the video, and make the process of question and video encoding executed in parallel. Furthermore, in addition to attending to the video features relevant to the given questions (i.e., video attention), we utilize the co-attention mechanism by simultaneously modeling “what words to listen to” (question attention). To the best of our knowledge, this is the first work of replacing RNNs with selfattention for the task of visual question answering. Experimental results of four tasks on the benchmark dataset show that our model significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task. Our model requires less computation time and achieves better performance compared with the RNNs-based methods. Additional ablation study demonstrates the effect of each component of our proposed model.

scholarly journals Development of Reading Comprehension System for Kannada Text Documents

2019 ◽  
Vol 8 (6S4) ◽  
pp. 42-45
Keyword(s):  
Reading Comprehension ◽  
Natural Language ◽  
Language Processing ◽  
Primary Language ◽  
Grammatical Structure ◽  
Text Documents ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Proposed Model ◽  
The Given

Reading Comprehension (RC) plays an important role in Natural Language Processing (NLP) as it reads and understands text written in Natural Language. Reading Comprehension systems comprehend the given document and answer questions in the context of the given document. This paper proposes a Reading Comprehension System for Kannada documents. The RC system analyses text in the Kannada script and allows users to pose questions to It in Kannada. This system is aimed at masses whose primary language is Kannada - who would otherwise have difficulties in parsing through vast Kannada documents for the information they require. This paper discusses the proposed model built using Term Frequency - Inverse Document Frequency (TF-IDF) and its performance in extracting the answers from the context document. The proposed model captures the grammatical structure of Kannada to provide the most accurate answers to the user

scholarly journals Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks

2019 ◽  
Author(s):  
Cheng Yang ◽  
Jian Tang ◽  
Maosong Sun ◽  
Ganqu Cui ◽  
Zhiyuan Liu
Keyword(s):  
Information Diffusion ◽  
State Of The Art ◽  
Sequential Data ◽  
Recurrent Networks ◽  
Multi Scale ◽  
Structural Context ◽  
Learning Techniques ◽  
Proposed Model ◽  
Real World Datasets ◽  
Diffusion Prediction

Information diffusion prediction is an important task which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction which aims at guessing the next influenced user or macroscopic diffusion prediction which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, no previous works have suggested a unified model for both microscopic and macroscopic scales. In this paper, we propose a novel multi-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the non-differentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.

scholarly journals Semantic Locality-Aware Deformable Network for Clothing Segmentation

2018 ◽  
Author(s):  
Wei Ji ◽  
Xi Li ◽  
Yueting Zhuang ◽  
Omar El Farouk Bourahla ◽  
Yixin Ji ◽  
...  
Keyword(s):  
Learning Process ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Small Sample ◽  
Sample Problem ◽  
Fine Grained ◽  
Domain Specific ◽  
Proposed Model ◽  
Segmentation Framework ◽  
Small Sample Problem

Clothing segmentation is a challenging vision problem typically implemented within a fine-grained semantic segmentation framework. Different from conventional segmentation, clothing segmentation has some domain-specific properties such as texture richness, diverse appearance variations, non-rigid geometry deformations, and small sample learning. To deal with these points, we propose a semantic locality-aware segmentation model, which adaptively attaches an original clothing image with a semantically similar (e.g., appearance or pose) auxiliary exemplar by search. Through considering the interactions of the clothing image and its exemplar, more intrinsic knowledge about the locality manifold structures of clothing images is discovered to make the learning process of small sample problem more stable and tractable. Furthermore, we present a CNN model based on the deformable convolutions to extract the non-rigid geometry-aware features for clothing images. Experimental results demonstrate the effectiveness of the proposed model against the state-of-the-art approaches.

scholarly journals Learning with Adaptive Neighbors for Image Clustering

2018 ◽  
Author(s):  
Yang Liu ◽  
Quanxue Gao ◽  
Zhaohua Yang ◽  
Shujian Wang
Keyword(s):  
State Of The Art ◽  
Clustering Algorithms ◽  
Original Data ◽  
Image Clustering ◽  
Complex Structures ◽  
Clustering Methods ◽  
Proposed Model ◽  
Data Graph ◽  
The Given ◽  
Optimal Graph

Due to the importance and efficiency of learning complex structures hidden in data, graph-based methods have been widely studied and get successful in unsupervised learning. Generally, most existing graph-based clustering methods require post-processing on the original data graph to extract the clustering indicators. However, there are two drawbacks with these methods: (1) the cluster structures are not explicit in the clustering results; (2) the final clustering performance is sensitive to the construction of the original data graph. To solve these problems, in this paper, a novel learning model is proposed to learn a graph based on the given data graph such that the new obtained optimal graph is more suitable for the clustering task. We also propose an efficient algorithm to solve the model. Extensive experimental results illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.

scholarly journals Draft and Edit: Automatic Storytelling Through Multi-Pass Hierarchical Conditional Variational Autoencoder

2020 ◽  
Vol 34 (02) ◽  
pp. 1741-1748 ◽  
Author(s):  
Meng-Hsuan Yu ◽  
Juntao Li ◽  
Danyang Liu ◽  
Dongyan Zhao ◽  
Rui Yan ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Hierarchical Structure ◽  
Language Processing ◽  
State Of The Art ◽  
Substantial Improvement ◽  
Proposed Model ◽  
Variational Autoencoder ◽  
The Hierarchical Structure

Automatic Storytelling has consistently been a challenging area in the field of natural language processing. Despite considerable achievements have been made, the gap between automatically generated stories and human-written stories is still significant. Moreover, the limitations of existing automatic storytelling methods are obvious, e.g., the consistency of content, wording diversity. In this paper, we proposed a multi-pass hierarchical conditional variational autoencoder model to overcome the challenges and limitations in existing automatic storytelling models. While the conditional variational autoencoder (CVAE) model has been employed to generate diversified content, the hierarchical structure and multi-pass editing scheme allow the story to create more consistent content. We conduct extensive experiments on the ROCStories Dataset. The results verified the validity and effectiveness of our proposed model and yields substantial improvement over the existing state-of-the-art approaches.

A text-based multi-span network for reading comprehension

2021 ◽  
pp. 1-13
Author(s):  
Deguang Chen ◽  
Ziping Ma ◽  
Lin Wei ◽  
Yanbin Zhu ◽  
Jinlin Ma ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Market Value ◽  
The State ◽  
Experimental Results ◽  
Training Time ◽  
The Public

Text-based reading comprehension models have great research significance and market value and are one of the main directions of natural language processing. Reading comprehension models of single-span answers have recently attracted more attention and achieved significant results. In contrast, multi-span answer models for reading comprehension have been less investigated and their performances need improvement. To address this issue, in this paper, we propose a text-based multi-span network for reading comprehension, ALBERT_SBoundary, and build a multi-span answer corpus, MultiSpan_NMU. We also conduct extensive experiments on the public multi-span corpus, MultiSpan_DROP, and our multi-span answer corpus, MultiSpan_NMU, and compare the proposed method with the state-of-the-art. The experimental results show that our proposed method achieves F1 scores of 84.10 and 92.88 on MultiSpan_DROP and MultiSpan_NMU datasets, respectively, while it also has fewer parameters and a shorter training time.

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