Aligning Source Visual and Target Language Domains for Unpaired Video Captioning

Training supervised video captioning model requires coupled video-caption pairs. However, for many targeted languages, sufficient paired data are not available. To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language. To solve the task, a natural choice is to employ a two-step pipeline system: first utilizing video-to-pivot captioning model to generate captions in pivot language and then utilizing pivot-to-target translation model to translate the pivot captions to the target language. However, in such a pipeline system, 1) visual information cannot reach the translation model, generating visual irrelevant target captions; 2) the errors in the generated pivot captions will be propagated to the translation model, resulting in disfluent target captions. To address these problems, we propose the Unpaired Video Captioning with Visual Injection system (UVC-VI). UVC-VI first introduces the Visual Injection Module (VIM), which aligns source visual and target language domains to inject the source visual information into the target language domain. Meanwhile, VIM directly connects the encoder of the video-to-pivot model and the decoder of the pivot-to-target model, allowing end-to-end inference by completely skipping the generation of pivot captions. To enhance the cross-modality injection of the VIM, UVC-VI further introduces a pluggable video encoder, i.e., Multimodal Collaborative Encoder (MCE). The experiments show that UVC-VI outperforms pipeline systems and exceeds several supervised systems. Furthermore, equipping existing supervised systems with our MCE can achieve 4% and 7% relative margins on the CIDEr scores to current state-of-the-art models on the benchmark MSVD and MSR-VTT datasets, respectively.Comment: Published at IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

A Comprehensive Approach to Automated Sign Language Translation

Many sign languages are bonafide natural languages with grammatical rules and lexicons, hence can benefit from neural machine translation methods. As significant advances are being made in natural language processing (specifically neural machine translation) and in computer vision processes, specifically image and video captioning, related methods can be further researched to boost automated sign language understanding. This is an especially challenging AI research area due to the involvement of a continuous visual-spatial modality, where meaning is often derived from context. To this end, this thesis is focused on the study and development of new computational methods and training mechanisms to enhance sign language translation in two directions, signs to texts and texts to signs. This work introduces a new, realistic phrase-level American Sign Language dataset (ASL/ ASLing), and investigates the role of different types of visual features (CNN embeddings, human body keypoints, and optical flow vectors) in translating ASL to spoken American English. Additionally, the research considers the role of multiple features for improved translation, via various fusion architectures. As an added benefit, with continuous sign language being challenging to segment, this work also explores the use of overlapping scaled visual segments, across the video, for simultaneously segmenting and translating signs. Finally, a quintessential interpreting agent not only understands sign language and translates to text, but also understands the text and translates to signs. Hence, to facilitate two-way sign language communication, i.e. visual sign to spoken language translation and spoken to visual sign language translation, a dual neural machine translation model, SignNet, is presented. Various training paradigms are investigated for improved translation, using SignNet. By exploiting the notion of similarity (and dissimilarity) of visual signs, a metric embedding learning process proved most useful in training SignNet. The resulting processes outperformed their state-of-the-art counterparts by showing noteworthy improvements in BLEU 1 - BLEU 4 scores

Object-Centric Unsupervised Image Captioning

Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performance in the age of deep learning. Most of these state-of-the-art, however, requires large volume of annotated image-caption pairs in order to train their models. When given an image dataset of interests, practitioner needs to annotate the caption for each image in the training set and this process needs to happen for each newly collected image dataset. In this paper, we explore the task of unsupervised image captioning which utilizes unpaired images and texts to train the model so that the texts can come from different sources than the images. A main school of research on this topic that has been shown to be effective is to construct pairs from the images and texts in the training set according to their overlap of objects. Unlike in the supervised setting, these constructed pairings are however not guaranteed to have fully overlapping set of objects. Our work in this paper overcomes this by harvesting objects corresponding to a given sentence from the training set, even if they don't belong to the same image. When used as input to a transformer, such mixture of objects enables larger if not full object coverage, and when supervised by the corresponding sentence, produced results that outperform current state of the art unsupervised methods by a significant margin. Building upon this finding, we further show that (1) additional information on relationship between objects and attributes of objects also helps in boosting performance; and (2) our method also extends well to non-English image captioning, which usually suffers from a scarcer level of annotations. Our findings are supported by strong empirical results. Our code is available at https://github.com/zihangm/obj-centric-unsup-caption.Comment: ECCV 202

TechNews digests: Jan - Mar 2010

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Towards Interaction-level Video Action Understanding

A huge amount of videos have been created, spread, and viewed daily. Among these massive videos, the actions and activities of humans account for a large part. We desire machines to understand human actions in videos as this is essential to various applications, including but not limited to autonomous driving cars, security systems, human-robot interactions and healthcare. Towards real intelligent system that is able to interact with humans, video understanding must go beyond simply answering ``what is the action in the video", but be more aware of what those actions mean to humans and be more in line with human thinking, which we call interactive-level action understanding. This thesis identifies three main challenges to approaching interactive-level video action understanding: 1) understanding actions given human consensus; 2) understanding actions based on specific human rules; 3) directly understanding actions in videos via human natural language. For the first challenge, we select video summary as a representative task that aims to select informative frames to retain high-level information based on human annotators' experience. Through self-attention architecture and meta-learning, which jointly process dual representations of visual and sequential information for video summarization, the proposed model is capable of understanding video from human consensus (e.g., how humans think which parts of an action sequence are essential). For the second challenge, our works on action quality assessment utilize transformer decoders to parse the input action into several sub-actions and assess the more fine-grained qualities of the given action, yielding the capability of action understanding given specific human rules. (e.g., how well a diving action performs, how well a robot performs surgery) The third key idea explored in this thesis is to use graph neural networks in an adversarial fashion to understand actions through natural language. We demonstrate the utility of this technique for the video captioning task, which takes an action video as input, outputs natural language, and yields state-of-the-art performance. It can be concluded that the research directions and methods introduced in this thesis provide fundamental components toward interactive-level action understanding

Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience studying during COVID 19 confinement periods matched the findings in recent research about the importance of digital accessibility through more e-learning intensive academic experiences. Narrative and systematic literature reviews enabled providing context in World Health Organization’s International Classification of Functioning, Disability and Health, legal and standards framework and information technology and communication state-of-the art. Assessing Portuguese higher education institutions’ web sites alerted to the fact that only outlying institutions implemented near perfect, accessibility-wise, websites. Therefore a gap was identified in how accessible the Portuguese higher education websites are, the needs of all students, including those with disabilities, and even the accessibility minimum legal requirements for digital products and the services provided by public or publicly funded organizations. Having identified a problem in society and exploring the scientific base of knowledge for context and state of the art was a first stage in the Design Science Research methodology, to which followed development and validation cycles of an Inclusive Intelligent Learning Management System Framework. The framework blends various Data Science study fields contributions with accessibility guidelines compliant interface design and content upload accessibility compliance assessment. Validation was provided by a focus group whose inputs were considered for the version presented in this dissertation. Not being the purpose of the research to deliver a complete implementation of the framework and lacking consistent data to put all the modules interacting with each other, the most relevant modules were tested with open data as proof of concept. The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute Scientific Repository and is to be completed with the publication of this thesis and the already started PhD’s findings in relevant journals and conferences