“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research.

Meta-Learning in Neural Networks: A Survey

This article addresses a timely problem, especially within the context of the Internet, which is accessible by anyone in any discipline from virtually anywhere in the world. The problem of cross-language retrieval is not new. Then at now, cross-language information retrieval was seen to be a function that would facilitate the effective search for, exchange of, and retrieval information. This chapter reviews research and practice in CLIR that allows users to state queries in their native language and retrieve documents in any other language supported by the system. CLIR can simplify searching by multilingual users and, if translation resources are limited, can allow monolingual users to allocate those resources to the more promising documents. This review begins with an examination of the literature on user needs for CLIR. The chapter largely follows the retrieval system model (document preprocessing, query formulation, matching, selection, and delivery). Each section highlights the unique requirements imposed on one more stages of the model in cross-language retrieval applications. The authors describe evaluation techniques and conclude with observations regarding future directions for CLIR research.

Cross-Language Information Retrieval.

This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (Damen in Scaling egocentric vision: ECCV, 2018), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.

/pdf/rescaling-egocentric-vision-collection-pipeline-and-1uc73skzdc.pdf

Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100

Our objective is to transform a video into a set of discrete audio-visual objects using self-supervised learning. To this end, we introduce a model that uses attention to localize and group sound sources, and optical flow to aggregate information over time. We demonstrate the effectiveness of the audio-visual object embeddings that our model learns by using them for four downstream speech-oriented tasks: (a) multi-speaker sound source separation, (b) localizing and tracking speakers, (c) correcting misaligned audio-visual data, and (d) active speaker detection. Using our representation, these tasks can be solved entirely by training on unlabeled video, without the aid of object detectors. We also demonstrate the generality of our method by applying it to non-human speakers, including cartoons and puppets.Our model significantly outperforms other self-supervised approaches, and obtains performance competitive with methods that use supervised face detection.

Self-Supervised Learning of Audio-Visual Objects from Video

Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual dataset for this task has constrained algorithm evaluations with respect to data diversity, environments, and accuracy. This has made comparisons and improvements difficult. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) that will be released publicly to facilitate algorithm development and enable comparisons. The dataset contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames or about 38.5 hours of face tracks, and the corresponding audio. We also present a new audio-visual approach for active speaker detection, and analyze its performance, demonstrating both its strength and the contributions of the dataset.

AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection

Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual active speaker dataset has limited evaluation in terms of data diversity, environments, and accuracy. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) which has been publicly released to facilitate algorithm development and comparison. It contains temporally labeled face tracks in videos, where each face instance is labeled as speaking or not, and whether the speech is audible. The dataset contains about 3.65 million human labeled frames spanning 38.5 hours. We also introduce a state-of-the-art, jointly trained audio-visual model for real-time active speaker detection and compare several variants. The evaluation clearly demonstrates a significant gain due to audio-visual modeling and temporal integration over multiple frames.

Ava Active Speaker: An Audio-Visual Dataset for Active Speaker Detection

We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation. The overview characterizes adaptation algorithms as based on embeddings, model parameter adaptation, or data augmentation. We present a meta-analysis of the performance of speech recognition adaptation algorithms, based on relative error rate reductions as reported in the literature.

/pdf/adaptation-algorithms-for-neural-network-based-speech-u8900fqwvo.pdf

Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

In this paper we present an extension of our previously described neural machine translation based system for punctuated transcription. This extension allows the system to map from per frame acoustic features to word level representations by replacing the traditional encoder in the encoder-decoder architecture with a hierarchical encoder. Furthermore, we show that a system combining lexical and acoustic features significantly outperforms systems using only a single source of features on all measured punctuation marks. The combination of lexical and acoustic features achieves a significant improvement in F-Measure of 1.5 absolute over the purely lexical neural machine translation based system.

/pdf/sequence-to-sequence-models-for-punctuated-transcription-f1cezbjc48.pdf

Sequence-to-sequence models for punctuated transcription combining lexical and acoustic features

In this paper we investigate the punctuated transcription of multi-genre broadcast media. We examine four systems, three of which are based on lexical features, the fourth of which uses acoustic features by integrating punctuation into the speech recognition acoustic models. We also explore the combination of these component systems using voting and log-linear interpolation. We performed experiments on the English language MGB Challenge data, which comprises about 1,600h of BBC television recordings. Our results indicate that a lexical system, based on a neural machine translation approach is significantly better than other systems achieving an F-Measure of 62.6% on reference text, with a relative degradation of 19% on ASR output. Our analysis of the results in terms of specific punctuation indicated that using longer context improves the prediction of question marks and acoustic information improves prediction of exclamation marks. Finally, we show that even though the systems are complementary, their straightforward combination does not yield better F-measures than a single system using neural machine translation.

/pdf/punctuated-transcription-of-multi-genre-broadcasts-using-4hd18t44r8.pdf

Ondrej Klejch

Papers

AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection

Ava Active Speaker: An Audio-Visual Dataset for Active Speaker Detection

Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

Sequence-to-sequence models for punctuated transcription combining lexical and acoustic features

Punctuated transcription of multi-genre broadcasts using acoustic and lexical approaches