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Author: Iftekhar Naim Publisher: ISBN: Category : Languages : en Pages : 127
Book Description
"Today we encounter large amounts of video data, often accompanied with text descriptions (e.g., cooking videos and recipes, videos of wetlab experiments and protocols, movies and scripts). Extracting meaningful information from these multimodal sequences requires aligning the video frames with the corresponding sentences in the text. Previous methods for connecting language and videos relied on manual annotations, which are often tedious and expensive to collect. In this thesis, we focus on automatically aligning sentences with the corresponding video frames without any direct human supervision. We first propose two hierarchical generative alignment models, which jointly align each sentence with the corresponding video frames, and each noun in a sentence with the corresponding object in the video frames. Next, we propose several latent-variable discriminative alignment models, which incorporate rich features involving verbs and video actions, and outperform the generative models. Our alignment algorithms are primarily applied to align biological wetlab videos with text instructions. Furthermore, we extend our alignment models for automatically aligning movie scenes with associated scripts and learning word-level translations between language pairs for which bilingual training data is unavailable. Thesis: By exploiting the temporal ordering constraints between video and associated text, it is possible to automatically align the sentences in the text with the corresponding video frames without any direct human supervision"--Pages vii.
Author: Iftekhar Naim Publisher: ISBN: Category : Languages : en Pages : 127
Book Description
"Today we encounter large amounts of video data, often accompanied with text descriptions (e.g., cooking videos and recipes, videos of wetlab experiments and protocols, movies and scripts). Extracting meaningful information from these multimodal sequences requires aligning the video frames with the corresponding sentences in the text. Previous methods for connecting language and videos relied on manual annotations, which are often tedious and expensive to collect. In this thesis, we focus on automatically aligning sentences with the corresponding video frames without any direct human supervision. We first propose two hierarchical generative alignment models, which jointly align each sentence with the corresponding video frames, and each noun in a sentence with the corresponding object in the video frames. Next, we propose several latent-variable discriminative alignment models, which incorporate rich features involving verbs and video actions, and outperform the generative models. Our alignment algorithms are primarily applied to align biological wetlab videos with text instructions. Furthermore, we extend our alignment models for automatically aligning movie scenes with associated scripts and learning word-level translations between language pairs for which bilingual training data is unavailable. Thesis: By exploiting the temporal ordering constraints between video and associated text, it is possible to automatically align the sentences in the text with the corresponding video frames without any direct human supervision"--Pages vii.
Author: Publisher: ISBN: 9781952363184 Category : Languages : en Pages : 74
Book Description
Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and emulate written or spoken human language. NLP draws from many disciplines including human-generated linguistic rules, machine learning, and deep learning to fill the gap between human communication and machine understanding. The papers included in this special collection demonstrate how NLP can be used to scale the human act of reading, organizing, and quantifying text data.
Author: Shai Avidan Publisher: Springer Nature ISBN: 3031200594 Category : Computers Languages : en Pages : 810
Book Description
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author: Qingshan Liu Publisher: Springer Nature ISBN: 9819984297 Category : Computers Languages : en Pages : 525
Book Description
The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.
Author: Gang Hua Publisher: Springer ISBN: 3319466046 Category : Computers Languages : en Pages : 930
Book Description
The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint Imagenet and MS Coco Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
Author: Tagyoung Chung Publisher: ISBN: Category : Languages : en Pages : 85
Book Description
"In a broad sense, latent information in natural language processing tasks refers to any information that is not plainly observable from raw data. Such latent information is found in abundance in many natural language processing tasks. Learning latent information itself could be the purpose of the task or it can be learned and utilized to improve relevant tasks. For example, in unsupervised learning of word alignment from parallel corpora, learning latent information is the task. Learning latent annotation for context free grammar falls into the latter category since latent annotation leads to better parsing accuracy. Depending on the availability of the data, latent information may be learned in a supervised manner or an unsupervised manner. This dissertation presents three different types of latent information that are learned and used to improve various natural language processing tasks, mainly focusing on different stages of machine translation. First, we discuss unsupervised learning of tokenization from parallel corpora using alignment between a bilingual sentence pair as latent information. Second, we examine using empty categories to improve parsing and machine translation. In these tasks, empty categories are latent information that are learned from raw text and applied to the respective tasks. Finally, we look at learning latent annotation for synchronous context free grammar, which leads us to more accurate and faster string-to-tree machine translation"--Page v.
Author: China Info & Comm Tech Grp Corp Publisher: Springer Nature ISBN: 9811619867 Category : Computers Languages : en Pages : 83
Book Description
This book is a part of the Blue Book series “Research on the Development of Electronic Information Engineering Technology in China”, which explores the cutting edge of natural language processing (NLP) studies. The research objects of natural language processing are evolved from words, phrases, and sentences to text, and research directions are from language analysis, language understanding, language generation, knowledge graphs, machine translation, to deep semantic understanding, and beyond. This is in line with the development trend of applications. And for another typical NLP application machine translation, from text translation, to voice and image translation, now simultaneous interpretation, progress of technology makes the application of machine translation deeper and wider into diverse industries. This book is intended for researchers and industrial staffs who have been following the current situation and future trends of the natural language processing. Meanwhile, it also bears high value of reference for experts, scholars, and technical and engineering managers of different levels and different fields.
Author: Iftekhar Naim
Author: Iftekhar Naim
Author: Zuxuan Wu
Author: 
Author: Shai Avidan
Author: Qingshan Liu
Author: Gang Hua
Author: Stevan Rudinac
Author: Tagyoung Chung
Author: Wenjie Zhang
Author: China Info & Comm Tech Grp Corp