This paper addresses the challenging task of video captioning which aims to generate descriptions for video data . Recently , the attention-based encoder-decoder structures have been widely used in video captioning . The Internet has a huge volume of unlabeled videos from diverse sources , making it difficult for video providers to organize and for viewers to consume the content . This paper defines the problem of video aboutness generation ( i.e. , the automatic generation of a concise natural-language description about a video ) and characterizes its differences from closely related problems such as video summarization and video caption . We then made an attempt to provide a solution to this problem . We present an approach that exploits hierarchical Recurrent Neural Networks ( RNNs ) to tackle the video captioning problem , i.e. , generating one or multiple sentences to describe a realistic video . Our hierarchical framework contains a sentence generator and a paragraph generator . Automatic video captioning is one of the ultimate challenges of Natural Language Processing , boosted by the omnipresence of video and the release of large-scale annotated video benchmarks . However , the specificity and quality of the captions vary considerably , having an adverse effect on the quality of the trained captioning models . In addition , this paper also briefly describes the most recent used datasets along with their limitations . Moreover , this review provides a detailed overview of the particular challenges existed in real-time video scene analysis that has been contributed towards activity recognition , scene interpretation , and video description/captioning . Finally , the paper summarizes the future trends and challenges in video scene analysis tasks and our insights are provided to inspire further research efforts . Attention models used for problems such as image captioning typically depend on the image under consideration , as well as the previous sequence of words that come before the word currently being generated . While these types of models have produced impressive results , they are not able to model the higher-order interactions involved in problems such as video description/captioning , where the relationship between parts of the video and the concepts being depicted is complex . Motivated by these observations , we propose a novel memory-based attention model for video description . Then , we further instantiate our hLSTMarefine it and apply it to the imioning task . To demonstrate the effectiveness of our proposed framework , we test our method on both video and image captioning tasks . Experimental results show that our approach achieves the state-of-the-art performance for most of the evaluation metrics on both tasks . We apply the new method to video captioning where temporal information plays a crucial role . Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks . We construct a novel gated fusion network , with one particularly designed cross-gating ( CG ) block , to effectively encode and fuse different types of representations , e.g. , the motion and content features of an input video . One POS sequence generator relies on this fused representation to predict the global syntactic structure , which is thereafter leveraged to guide the video captioning generation and control the syntax of the generated sentence . Specifically , a gating strategy is proposed to dynamically and adaptively incorporate the global syntactic POS information into the decoder for generating each word . The explosion of video data on the internet requires effective and efficient technology to generate captions automatically for people who are not able to watch the videos . Despite the great progress of video captioning research , particularly on video feature encoding , the language decoder is still largely based on the prevailing RNN decoder such as LSTM , which tends to prefer the frequent word that aligns with the video . In this paper , we propose a boundary-aware hierarchical language decoder for video captioning , which consists of a high-level GRU based language decoder , working as a global ( caption-level ) language model , and a low-level GRU based language decoder , working as a local ( phrase-level ) language model . Then , based on the prompt , the agent could generate a more accurate caption . We name this process a new task of video interactive captioning ( ViCap ) . Taking a video and an initial caption as input , we devise the ViCap agent which consists of a video encoder , an initial caption encoder , and a refined caption generator . The preprocessor presented in this paper is capable of dealing with vowelized and unvowelized words , and provides the input words along with relevant linguistics information needed by different applications . It is designed to be used with different NLP applications such as machine translation , text summarization , text correction , information retrieval , and automatic vowelization of Arabic text . Rozovskaya Alla ; Houda Bouamor ; Nizar Habash ; Wajdi Zaghouani ; Ossama Obeid ; Behrang Mohit . The Second QALB Shared Task on Automatic Text Correction for Arabic . In Proceedings of the ACL 2015 Workshop on Arabic Natural Language Processing ( ANLP ) , Beijing , China , July 2015 . - Le Lexique " BDLEX " Du Francais Ecrit Et Oral . In this paper , we describe lexical needs for spoken and written French surface processing , like automatic text correction , speech recognition and synthesis . We present statistical observations made on a vocabulary compiled from real texts like articles . This paper describes the results of initial work aimed at increasing the number and improving the quality of textual versions of the historical documents available in Polish digital libraries . Digital libraries community is missing tools that integrate existing digitisation workflow with customizable OCR engine and crowd --- based text correction , this paper describes work on providing such a solution . Apart from today 's state of the art in this field , this paper includes a description of the Virtual Transcription Laboratory ( VTL ) prototype , a crowdsourcing platform that utilize the Tesseract OCR engine . Automized diagnosis and spelling tutoring of children can play an important role in raising their orthographic level of competence . One of several necessary steps in an automatic spelling tutoring system is the automatic correction of achieved text that was freely written by children and contains errors . Based on the common knowledge that children in the first years of school write as they speak , we propose a novel , context-sensitive spelling correction algorithm that uses phonetic similarities , in order to achieve this step . It also compares results of insertion and deletion error results of ‘ Addak ’ and ‘ Bindi’and its overall contribution in total number of errors . It also presents a brief overview of difficulties in automatic text error correction in Punjabi Typed Text . This paper is based on the analysis done on 20000 misspelled words generated by typists . Enhancement of OCR tools or manual text correction is crucial to the further development of data mining . Social networking tools may be utilized to expand OCR text correction to users and for tagging maps and illustrations . Scientists are providing bibliographies for taxonomic groups that can be incorporated into the selection process . This paper proposes a rapid method to accomplish text images correction from slant ones . In the method , the well-known projection algorithm is presented as basic principle . This paper introduces a new problem , called Visual Text Correction ( VTC ) , i.e. , finding and replacing an inaccurate word in the textual description of a video . We propose a deep network that can simultaneously detect an inaccuracy in a sentence , and fix it by replacing the inaccurate word(s ) . Our experiments and performance analysis demonstrates that the proposed method provides very good results and also highlights the general challenges in solving the VTC problem . To the best of our knowledge , this work is the first of its kind for the Visual Text Correction task . Visual captioning , the task of describing an image or a video using one or few sentences , is challenging owing to the complexity of understanding copious visual information and describing it using natural language . Motivated by the success neural machine translation , previous work applies sequence to sequence learning to translate videos into sentences . This task , often referred to as image or visual captioning , forms the technical foundation of many important applications , such as semantic visual search , visual intelligence in chatting robots , photo and video sharing in social media , and aid for visually impaired people to perceive surrounding visual content . Thanks to the recent advances in deep learning , the AI research community has witnessed tremendous progress in visual captioning in recent years . In this article , we will first summarize this exciting emerging visual captioning area . Captioning has attracted much attention in image and video understanding while little work examines audio captioning . This paper contributes a manually-annotated dataset on car scene , in extension to a previously published hospital audio captioning dataset . It is an intermodal translation task ( not speech-to-text ) , where a system accepts as an input an audio signal and outputs the textual description ( i.e. the caption ) of that signal . In this paper we present Clotho , a dataset for audio captioning consisting of 4981 audio samples of 15 to 30 seconds duration and 24 905 captions of eight to 20 words length , and a baseline method to provide initial results . Clotho is built with focus on audio content and caption diversity , and the splits of the data are not hampering the training or evaluation of methods . Experiments show remarkable improvements in the performance on the old tasks while the figures for the new surprisingly surpass fine-tuning . Our framework also offers a scalable solution for continual image or video captioning . Neural attention ( NA ) has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization ( ADS ) and video captioning ( VC ) . NA mechanisms perform inference of context vectors ; these constitute weighted sums of deterministic input sequence encodings , adaptively sourced over long temporal horizons . To ensure a high quality dataset , we developed a series of novel quality controls to validate free-form text input from crowd-workers . We show that there is unambiguous association between visual content and natural language descriptions in our dataset , making it an ideal benchmark for the visual content captioning task . We perform extensive statistical analyses to compare our dataset to existing image and video description datasets . Localizing temporal action proposals from long videos is a fundamental challenge in video analysis ( e.g. , action detection and recognition or dense video captioning ) . Most existing approaches often overlook the hierarchical granularities of actions and thus fail to discriminate fine-grained action proposals ( e.g. , hand washing laundry or changing a tire in vehicle repair ) . The goal is to densely detect visual concepts ( e.g. , objects , object parts , and interactions between them ) from images , labeling each with a short descriptive phrase . We identify two key challenges of dense captioning that need to be properly addressed when tackling the problem . First , dense visual concept annotations in each image are associated with highly overlapping target regions , making accurate localization of each visual concept challenging . A new multimodal object description network ( MODN ) model for dense captioning is proposed . The proposed model is constructed by using a vision module and a language module . To capture the dependencies between the events in a video , our model introduces a new captioning module that uses contextual information from past and future events to jointly describe all events . We also introduce ActivityNet Captions , a large-scale benchmark for dense-captioning events . ActivityNet Captions contains 20k videos amounting to 849 video hours with 100k total descriptions , each with its unique start and end time . Finally , we report performances of our model for dense-captioning events , video retrieval and localization . Based on recent developments in dense image captioning , it is now possible to describe every object of a photographed scene with a caption while objects are determined by bounding boxes . However , the user interpretation of such an output is not trivial due to the existence of many overlapping bounding boxes . Motivated by video dense captioning , we propose a model to generate procedure captions from narrated instructional videos which are a sequence of stepwise clips with description . Previous works on video dense captioning learn video segments and generate captions without considering transcripts . We argue that transcripts in narrated instructional videos can enhance video representation by providing fine-grained complimentary and semantic textual information . This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019 : trimmed action recognition , dense-captioning events in videos , and spatio-temporal action localization . In this paper , we aim to tackle the task of captioning each event in one video ( dense captioning in videos ) and propose a novel pipeline . The task is challenging because of the uncertainty of event measurement as well as generating context-aware sentences . We propose five types of contexts as well as two categories of event captioning models , and evaluate their contributions for event captioning from both accuracy and diversity aspects . The proposed captioning models are plugged into our pipeline system for the dense video captioning challenge . The overall system achieves the state-of-the-art performance on the dense-captioning events in video task with 9.91 METEOR score on the challenge testing set . The extensive experiments on two static-vs-dynamic saliency datasets collected by us show that our proposed method outperforms the state-of-the-art methods for dynamic saliency prediction . Finally , we also introduce the application of dynamic saliency prediction for dynamic video captioning , assisting people with hearing impairments to better entertain videos with only off-screen voices , e.g. , documentary films , news videos and sports videos .
This paper addresses the challenging task of video captioning which aims to generate descriptions for video data . Recently , the attention-based encoder-decoder structures have been widely used in video captioning .
The Internet has a huge volume of unlabeled videos from diverse sources , making it difficult for video providers to organize and for viewers to consume the content . This paper defines the problem of video aboutness generation ( i.e. , the automatic generation of a concise natural-language description about a video ) and characterizes its differences from closely related problems such as video summarization and video caption . We then made an attempt to provide a solution to this problem .
We present an approach that exploits hierarchical Recurrent Neural Networks ( RNNs ) to tackle the video captioning problem , i.e. , generating one or multiple sentences to describe a realistic video . Our hierarchical framework contains a sentence generator and a paragraph generator .
Automatic video captioning is one of the ultimate challenges of Natural Language Processing , boosted by the omnipresence of video and the release of large-scale annotated video benchmarks . However , the specificity and quality of the captions vary considerably , having an adverse effect on the quality of the trained captioning models .
In addition , this paper also briefly describes the most recent used datasets along with their limitations . Moreover , this review provides a detailed overview of the particular challenges existed in real-time video scene analysis that has been contributed towards activity recognition , scene interpretation , and video description/captioning . Finally , the paper summarizes the future trends and challenges in video scene analysis tasks and our insights are provided to inspire further research efforts .
Attention models used for problems such as image captioning typically depend on the image under consideration , as well as the previous sequence of words that come before the word currently being generated . While these types of models have produced impressive results , they are not able to model the higher-order interactions involved in problems such as video description/captioning , where the relationship between parts of the video and the concepts being depicted is complex . Motivated by these observations , we propose a novel memory-based attention model for video description .
Then , we further instantiate our hLSTMarefine it and apply it to the imioning task . To demonstrate the effectiveness of our proposed framework , we test our method on both video and image captioning tasks . Experimental results show that our approach achieves the state-of-the-art performance for most of the evaluation metrics on both tasks .
We apply the new method to video captioning where temporal information plays a crucial role . Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks .
We construct a novel gated fusion network , with one particularly designed cross-gating ( CG ) block , to effectively encode and fuse different types of representations , e.g. , the motion and content features of an input video . One POS sequence generator relies on this fused representation to predict the global syntactic structure , which is thereafter leveraged to guide the video captioning generation and control the syntax of the generated sentence . Specifically , a gating strategy is proposed to dynamically and adaptively incorporate the global syntactic POS information into the decoder for generating each word .
The explosion of video data on the internet requires effective and efficient technology to generate captions automatically for people who are not able to watch the videos . Despite the great progress of video captioning research , particularly on video feature encoding , the language decoder is still largely based on the prevailing RNN decoder such as LSTM , which tends to prefer the frequent word that aligns with the video . In this paper , we propose a boundary-aware hierarchical language decoder for video captioning , which consists of a high-level GRU based language decoder , working as a global ( caption-level ) language model , and a low-level GRU based language decoder , working as a local ( phrase-level ) language model .
Then , based on the prompt , the agent could generate a more accurate caption . We name this process a new task of video interactive captioning ( ViCap ) . Taking a video and an initial caption as input , we devise the ViCap agent which consists of a video encoder , an initial caption encoder , and a refined caption generator .
The preprocessor presented in this paper is capable of dealing with vowelized and unvowelized words , and provides the input words along with relevant linguistics information needed by different applications . It is designed to be used with different NLP applications such as machine translation , text summarization , text correction , information retrieval , and automatic vowelization of Arabic text .
Rozovskaya Alla ; Houda Bouamor ; Nizar Habash ; Wajdi Zaghouani ; Ossama Obeid ; Behrang Mohit . The Second QALB Shared Task on Automatic Text Correction for Arabic . In Proceedings of the ACL 2015 Workshop on Arabic Natural Language Processing ( ANLP ) , Beijing , China , July 2015 .
- Le Lexique " BDLEX " Du Francais Ecrit Et Oral . In this paper , we describe lexical needs for spoken and written French surface processing , like automatic text correction , speech recognition and synthesis . We present statistical observations made on a vocabulary compiled from real texts like articles .
This paper describes the results of initial work aimed at increasing the number and improving the quality of textual versions of the historical documents available in Polish digital libraries . Digital libraries community is missing tools that integrate existing digitisation workflow with customizable OCR engine and crowd --- based text correction , this paper describes work on providing such a solution . Apart from today 's state of the art in this field , this paper includes a description of the Virtual Transcription Laboratory ( VTL ) prototype , a crowdsourcing platform that utilize the Tesseract OCR engine .
Automized diagnosis and spelling tutoring of children can play an important role in raising their orthographic level of competence . One of several necessary steps in an automatic spelling tutoring system is the automatic correction of achieved text that was freely written by children and contains errors . Based on the common knowledge that children in the first years of school write as they speak , we propose a novel , context-sensitive spelling correction algorithm that uses phonetic similarities , in order to achieve this step .
It also compares results of insertion and deletion error results of ‘ Addak ’ and ‘ Bindi’and its overall contribution in total number of errors . It also presents a brief overview of difficulties in automatic text error correction in Punjabi Typed Text . This paper is based on the analysis done on 20000 misspelled words generated by typists .
Enhancement of OCR tools or manual text correction is crucial to the further development of data mining . Social networking tools may be utilized to expand OCR text correction to users and for tagging maps and illustrations . Scientists are providing bibliographies for taxonomic groups that can be incorporated into the selection process .
This paper proposes a rapid method to accomplish text images correction from slant ones . In the method , the well-known projection algorithm is presented as basic principle .
This paper introduces a new problem , called Visual Text Correction ( VTC ) , i.e. , finding and replacing an inaccurate word in the textual description of a video . We propose a deep network that can simultaneously detect an inaccuracy in a sentence , and fix it by replacing the inaccurate word(s ) .
Our experiments and performance analysis demonstrates that the proposed method provides very good results and also highlights the general challenges in solving the VTC problem . To the best of our knowledge , this work is the first of its kind for the Visual Text Correction task .
Visual captioning , the task of describing an image or a video using one or few sentences , is challenging owing to the complexity of understanding copious visual information and describing it using natural language . Motivated by the success neural machine translation , previous work applies sequence to sequence learning to translate videos into sentences .
This task , often referred to as image or visual captioning , forms the technical foundation of many important applications , such as semantic visual search , visual intelligence in chatting robots , photo and video sharing in social media , and aid for visually impaired people to perceive surrounding visual content . Thanks to the recent advances in deep learning , the AI research community has witnessed tremendous progress in visual captioning in recent years . In this article , we will first summarize this exciting emerging visual captioning area .
Captioning has attracted much attention in image and video understanding while little work examines audio captioning . This paper contributes a manually-annotated dataset on car scene , in extension to a previously published hospital audio captioning dataset .
It is an intermodal translation task ( not speech-to-text ) , where a system accepts as an input an audio signal and outputs the textual description ( i.e. the caption ) of that signal . In this paper we present Clotho , a dataset for audio captioning consisting of 4981 audio samples of 15 to 30 seconds duration and 24 905 captions of eight to 20 words length , and a baseline method to provide initial results . Clotho is built with focus on audio content and caption diversity , and the splits of the data are not hampering the training or evaluation of methods .
Experiments show remarkable improvements in the performance on the old tasks while the figures for the new surprisingly surpass fine-tuning . Our framework also offers a scalable solution for continual image or video captioning .
Neural attention ( NA ) has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization ( ADS ) and video captioning ( VC ) . NA mechanisms perform inference of context vectors ; these constitute weighted sums of deterministic input sequence encodings , adaptively sourced over long temporal horizons .
To ensure a high quality dataset , we developed a series of novel quality controls to validate free-form text input from crowd-workers . We show that there is unambiguous association between visual content and natural language descriptions in our dataset , making it an ideal benchmark for the visual content captioning task . We perform extensive statistical analyses to compare our dataset to existing image and video description datasets .
Localizing temporal action proposals from long videos is a fundamental challenge in video analysis ( e.g. , action detection and recognition or dense video captioning ) . Most existing approaches often overlook the hierarchical granularities of actions and thus fail to discriminate fine-grained action proposals ( e.g. , hand washing laundry or changing a tire in vehicle repair ) .
The goal is to densely detect visual concepts ( e.g. , objects , object parts , and interactions between them ) from images , labeling each with a short descriptive phrase . We identify two key challenges of dense captioning that need to be properly addressed when tackling the problem . First , dense visual concept annotations in each image are associated with highly overlapping target regions , making accurate localization of each visual concept challenging .
A new multimodal object description network ( MODN ) model for dense captioning is proposed . The proposed model is constructed by using a vision module and a language module .
To capture the dependencies between the events in a video , our model introduces a new captioning module that uses contextual information from past and future events to jointly describe all events . We also introduce ActivityNet Captions , a large-scale benchmark for dense-captioning events . ActivityNet Captions contains 20k videos amounting to 849 video hours with 100k total descriptions , each with its unique start and end time . Finally , we report performances of our model for dense-captioning events , video retrieval and localization .
Based on recent developments in dense image captioning , it is now possible to describe every object of a photographed scene with a caption while objects are determined by bounding boxes . However , the user interpretation of such an output is not trivial due to the existence of many overlapping bounding boxes .
Motivated by video dense captioning , we propose a model to generate procedure captions from narrated instructional videos which are a sequence of stepwise clips with description . Previous works on video dense captioning learn video segments and generate captions without considering transcripts . We argue that transcripts in narrated instructional videos can enhance video representation by providing fine-grained complimentary and semantic textual information .
This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019 : trimmed action recognition , dense-captioning events in videos , and spatio-temporal action localization .
In this paper , we aim to tackle the task of captioning each event in one video ( dense captioning in videos ) and propose a novel pipeline . The task is challenging because of the uncertainty of event measurement as well as generating context-aware sentences .
We propose five types of contexts as well as two categories of event captioning models , and evaluate their contributions for event captioning from both accuracy and diversity aspects . The proposed captioning models are plugged into our pipeline system for the dense video captioning challenge . The overall system achieves the state-of-the-art performance on the dense-captioning events in video task with 9.91 METEOR score on the challenge testing set .
The extensive experiments on two static-vs-dynamic saliency datasets collected by us show that our proposed method outperforms the state-of-the-art methods for dynamic saliency prediction . Finally , we also introduce the application of dynamic saliency prediction for dynamic video captioning , assisting people with hearing impairments to better entertain videos with only off-screen voices , e.g. , documentary films , news videos and sports videos .
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning Leveraging Multi-modal Analyses and Online Knowledge Base for Video Aboutness Generation Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks Improving the Quality of Video-to-Language Models by Optimizing Annotation of the Training Material Video scene analysis: an overview and challenges on deep learning algorithms Memory-augmented Attention Modelling for Videos Hierarchical LSTMs with Adaptive Attention for Visual Captioning Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning Controllable Video Captioning With POS Sequence Guidance Based on Gated Fusion Network Video Captioning with Boundary-aware Hierarchical Language Decoding and Joint Video Prediction Video Interactive Captioning with Human Prompts Arabic Text Preprocessing for the Natural Language Processing Applications Annotation Guidelines and Framework for Arabic Machine Translation Post-Edited Corpus Besoins Lexicaux A La Lumiere De L'Analyse Statistique Du Corpus De Textes Du Projet "Bref" - Le Lexique "BDLEX" Du Francais Ecrit Et Oral Creation of Textual Versions of Historical Documents from Polish Digital Libraries Towards Context-Dependent Phonetic Spelling Error Correction in Children's Freely Composed Text for Diagnostic and Pedagogical Purposes Contribution of ‘Addak’and ‘Bindi’ in Non word Error Pattern analysis of Punjabi Typed Text BHL, The Biodiversity Heritage Library: An Expanding International Collaboration A Fast Method of Calibrating Slant Text Image Based on Projection Algorithm Visual Text Correction SibNet: Sibling Convolutional Encoder for Video Captioning. Deep Learning for Image-to-Text Generation: A Technical Overview What does a Car-ssette tape tell? Clotho: an Audio Captioning Dataset ContCap: A scalable framework for continual image captioning. Amortized Context Vector Inference for Sequence-to-Sequence Networks TGIF: A New Dataset and Benchmark on Animated GIF Description Coarse-to-Fine Localization of Temporal Action Proposals Dense Captioning with Joint Inference and Visual Context Multimodal object description network for dense captioning Dense-Captioning Events in Videos Deep Interactive Region Segmentation and Captioning Dense Procedure Captioning in Narrated Instructional Videos Trimmed Action Recognition, Dense-Captioning Events in Videos, and Spatio-temporal Action Localization with Focus on ActivityNet Challenge 2019 Hierarchical Context Encoding for Events Captioning in Videos Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos Static saliency vs. dynamic saliency: a comparative study
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2019 2016 2015 2018 2018 2017 2018 2015 2019 2018 2019 2007 2016 1992 2012 2011 2017 2009 2010 2018 2020 2017 2019 2019 2020 2018 2016 2020 2017 2017 2017 2017 2019 2019 2018 2019 2013
EMNLP/IJCNLP (1) ISVC (2) MMM (1) IJCAI COLING TPDL INTERSPEECH WKDD ECCV (13) CVPR ACL (1) ICIP ACM Multimedia
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