by jsendak | Jan 10, 2024 | Computer Science
Audio and video are two most common modalities in the mainstream media
platforms, e.g., YouTube. To learn from multimodal videos effectively, in this
work, we propose a novel audio-video recognition approach termed audio video
Transformer, AVT, leveraging the effective spatio-temporal representation by
the video Transformer to improve action recognition accuracy. For multimodal
fusion, simply concatenating multimodal tokens in a cross-modal Transformer
requires large computational and memory resources, instead we reduce the
cross-modality complexity through an audio-video bottleneck Transformer. To
improve the learning efficiency of multimodal Transformer, we integrate
self-supervised objectives, i.e., audio-video contrastive learning, audio-video
matching, and masked audio and video learning, into AVT training, which maps
diverse audio and video representations into a common multimodal representation
space. We further propose a masked audio segment loss to learn semantic audio
activities in AVT. Extensive experiments and ablation studies on three public
datasets and two in-house datasets consistently demonstrate the effectiveness
of the proposed AVT. Specifically, AVT outperforms its previous
state-of-the-art counterparts on Kinetics-Sounds by 8%. AVT also surpasses one
of the previous state-of-the-art video Transformers [25] by 10% on VGGSound by
leveraging the audio signal. Compared to one of the previous state-of-the-art
multimodal methods, MBT [32], AVT is 1.3% more efficient in terms of FLOPs and
improves the accuracy by 3.8% on Epic-Kitchens-100.
In this article, the authors propose a novel approach called audio video Transformer (AVT) to effectively learn from multimodal videos. They aim to improve action recognition accuracy by leveraging the spatio-temporal representation provided by the video Transformer. However, instead of simply concatenating multimodal tokens in a cross-modal Transformer, they introduce an audio-video bottleneck Transformer to reduce computational and memory resources required for multimodal fusion.
One interesting aspect of this approach is the integration of self-supervised objectives into AVT training. This includes audio-video contrastive learning, audio-video matching, and masked audio and video learning. By mapping diverse audio and video representations into a common multimodal representation space, they enhance the learning efficiency of the multimodal Transformer.
The authors also propose a masked audio segment loss to specifically learn semantic audio activities in AVT. This is a valuable addition as it allows for more nuanced understanding of the audio component in multimodal videos.
The experimental results and ablation studies conducted on various datasets show the effectiveness of AVT. It outperforms previous state-of-the-art approaches on Kinetics-Sounds by 8% and on VGGSound by 10% by leveraging the audio signal. Additionally, compared to a previous multimodal method called MBT, AVT is more efficient in terms of FLOPs and improves accuracy by 3.8% on Epic-Kitchens-100.
This work demonstrates the multi-disciplinary nature of multimedia information systems and its intersection with concepts such as animations, artificial reality, augmented reality, and virtual realities. The effective recognition and understanding of audio and video content in multimodal videos have significant implications in various fields, including entertainment, education, healthcare, and communication.
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by jsendak | Jan 7, 2024 | Computer Science
Analysis of the Article: Generating Artificial Multivariate Time Series Signals with a Transformer-Based Autoencoder
The article discusses the importance of developing robust representations of training data for trustworthy machine learning. It highlights the use of Generative Adversarial Networks (GANs) in generating realistic data, particularly in the field of image generation. However, the article points out that less attention has been given to generating time series data, especially multivariate signals. To address this gap, the article proposes a Transformer-based autoencoder that is regularized through an adversarial training scheme to generate artificial multivariate time series signals.
One key contribution of this work is the use of a Transformer-based architecture for generating time series signals. Transformers have shown excellent performance in natural language processing tasks and have recently gained attention in computer vision tasks as well. The adoption of Transformers for generating time series data is a novel approach that brings the potential for capturing long-term dependencies and complex patterns.
The article suggests that using a Transformer-based autoencoder with adversarial regularization leads to improved generation of multivariate time series signals compared to a convolutional network approach. To support this claim, the authors evaluate the generated signals using t-SNE visualizations, Dynamic Time Warping (DTW), and Entropy scores.
- t-SNE visualizations are commonly used to visualize high-dimensional data in a lower-dimensional space, leading to clusters that represent similar patterns or instances. By comparing the t-SNE visualizations of the generated signals with an exemplary dataset, the authors can assess their similarity.
- Dynamic Time Warping (DTW) is a measure of similarity between two time series signals. By calculating DTW scores between the generated signals and the examples in the dataset, the authors can quantitatively evaluate their similarity.
- Entropy scores are used to measure the randomness of a time series signal. By comparing the entropy scores of the generated signals and the exemplar dataset, the authors can assess the quality and diversity of the generated signals.
Overall, this research presents a valuable contribution to the generation of artificial multivariate time series signals. By leveraging Transformer-based architectures and adversarial regularization, the proposed method demonstrates improved performance compared to traditional convolutional network approaches. The evaluation metrics used provide a comprehensive analysis of the generated signals’ similarity and quality. Future research could explore the application of this approach to different domains and further investigate the interpretability of the generated signals for real-world applications.
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by jsendak | Jan 7, 2024 | AI
This paper describes approaches and results for shared Task 1 and 4 of
SMMH4-23 by Team Shayona. Shared Task-1 was binary classification of english
tweets self-reporting a COVID-19 diagnosis, and Shared Task-4 was Binary
classification of English Reddit posts self-reporting a social anxiety disorder
diagnosis. Our team has achieved the highest f1-score 0.94 in Task-1 among all
participants. We have leveraged the Transformer model (BERT) in combination
with the LightGBM model for both tasks.
Expert Commentary: Leveraging Transformer Models for Binary Classification Tasks
In this article, we will discuss the approaches and results achieved by Team Shayona in the shared Task 1 and Task 4 of SMMH4-23. Task 1 involved the binary classification of English tweets that self-reported a COVID-19 diagnosis, while Task 4 focused on the binary classification of English Reddit posts that self-reported a social anxiety disorder diagnosis. Team Shayona successfully achieved the highest f1-score of 0.94 in Task 1 among all participants.
What makes Team Shayona’s achievement particularly noteworthy is their utilization of the Transformer model, specifically BERT (Bidirectional Encoder Representations from Transformers), in combination with the LightGBM model for both tasks. This approach showcases the multi-disciplinary nature of these concepts, as it combines techniques from both natural language processing (NLP) and machine learning domains.
BERT, a popular transformer-based model, has revolutionized many NLP tasks by capturing deep contextual information and overcoming the limitations of traditional word-level embeddings. By leveraging BERT, Team Shayona was able to extract rich semantic representations from the textual data, enabling them to better understand the nuanced language used in tweets and Reddit posts related to COVID-19 and social anxiety disorder.
Furthermore, by combining BERT with LightGBM, which is a gradient boosting framework, Team Shayona effectively incorporated both deep learning and ensemble learning techniques into their approach. This combination likely helped them overcome potential shortcomings of using BERT alone, such as computational costs and sensitivity to hyperparameters. LightGBM’s ability to handle large-scale datasets and its efficient training process likely contributed to the team’s excellent performance.
The success of Team Shayona highlights the importance of leveraging state-of-the-art models like BERT in conjunction with other powerful machine learning algorithms to achieve superior results in binary classification tasks. The ability to analyze and classify user-generated content related to COVID-19 and mental health disorders holds significant value in various domains, including healthcare, public health, and social sciences.
In future iterations of similar tasks, it would be interesting to see how Team Shayona’s approach can be further optimized. Exploring different transformer-based models, such as GPT-3 or RoBERTa, may offer additional insights into the data and potentially improve the classification performance. Additionally, fine-tuning the hyperparameters of BERT and LightGBM could lead to enhanced results, as these models often rely on careful parameter tuning for optimal performance.
In conclusion
Team Shayona’s achievement in the shared Task 1 and Task 4 of SMMH4-23 demonstrates the value of utilizing transformer models, like BERT, in combination with other machine learning approaches for binary classification tasks. This multi-disciplinary approach showcases the potential of combining NLP and machine learning techniques to gain deeper insights from textual data related to COVID-19 and mental health disorders. As the field progresses, further exploration of different transformer-based models and hyperparameter optimization will likely lead to even more impressive results.
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by jsendak | Jan 6, 2024 | Namecheap
As we continue to navigate the intricate and ever-changing terrain of search engine optimisation (SEO), one particular development has recently caught the attention of professionals in the field. This is none other than Google’s most recent algorithm update— Bidirectional Encoder Representations from Transformers or more commonly known as, BERT.
Understanding Google’s BERT
In this incisive article, we delve into both the underlying mechanisms and larger significance of BERT, a system that uses machine learning and natural language processing techniques to comprehend the nuances and context of words in searches, thereby providing more relevant results.
Tuning Your Content for BERT
However, aligning your content to meet the semantic demands of Google’s BERT doesn’t have to be a shot in the dark. In fact, we’ve outlined a comprehensive guide showcasing how you can fine-tune your content in order to adapt effectively to this dynamic new phase in SEO.
Key Factors to Consider
We’ll also provide an exhaustive list of key factors to consider when attempting to optimise your site for BERT, with each examination not only highlighting the pivotal role of these elements but also offering insights on how you can incorporate them into your overall SEO strategy.
Let’s Unpack
In this journey of decoding Google’s BERT, let’s dive in together, explore its potential implications for SEO, and understand how we can rise to meet the challenges and capitalize on the opportunities it presents.
Let’s unpack what Google’s BERT entails for SEO and how you can fine-tune your content for this dynamic new phase.
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by jsendak | Jan 5, 2024 | AI
In recent years, foundation models (FMs) have solidified their role as cornerstone advancements in the deep learning domain. By extracting intricate patterns from vast datasets, these models…
have revolutionized various fields such as natural language processing, computer vision, and speech recognition. Foundation models, also known as pre-trained models, have proven to be highly effective in solving complex problems by leveraging their ability to learn from extensive amounts of data. This article explores the significance of foundation models in deep learning and delves into their applications, benefits, and challenges. From their ability to understand and generate human-like text to their potential in democratizing AI, foundation models have emerged as a game-changer in the world of artificial intelligence.
In recent years, foundation models (FMs) have solidified their role as cornerstone advancements in the deep learning domain. By extracting intricate patterns from vast datasets, these models have revolutionized the way we approach complex problems and enhance machine learning capabilities. However, just like any technological breakthrough, FMs bring along their own set of challenges and limitations.
The Challenge of Interpretability
One of the key challenges posed by FMs is the lack of interpretability. While these models are excellent at identifying patterns and making predictions, understanding how they arrived at those conclusions is often a black box. This lack of transparency raises ethical concerns in critical domains like healthcare and finance, where decision-making based on opaque algorithms may result in biased outcomes or limited accountability.
An innovative solution to this problem lies in the concept of Explainable Artificial Intelligence (XAI). By integrating XAI techniques into FMs, we can unlock the underlying reasoning and decision-making processes. Techniques such as rule extraction, feature importance analysis, and attention mechanisms can shed light on the inner workings of these models, enabling users to trust and interpret their outputs.
Data Privacy and Security
Another pressing concern with FMs revolves around data privacy and security. These models require vast amounts of data to achieve optimal performance, which often includes sensitive information. As we increasingly rely on FMs to handle personal, business, and societal data, it becomes crucial to ensure robust privacy-preserving measures.
One innovative solution lies in federated learning, where the model is trained collaboratively across multiple decentralized devices or servers. By keeping the data at its source and only sharing updates rather than raw information, federated learning mitigates concerns regarding data exposure and centralized vulnerabilities. This approach not only protects privacy but also allows for a more distributed and resilient learning network.
Addressing Bias and Fairness
Bias and fairness have been persistent issues in machine learning, and FMs are not exempt from this challenge. Training these models on biased or unrepresentative data can lead to unjust outcomes and perpetuate existing inequalities. To ensure fairness and reduce bias in FM applications, it is crucial to adopt innovative solutions that address these concerns head-on.
One approach is to proactively curate diverse and representative datasets during the model development phase. Collaborating with domain experts and stakeholders can help identify potential biases and develop robust strategies to mitigate them. Additionally, ongoing monitoring and evaluation of FM performance can uncover bias patterns and prompt necessary adjustments to improve fairness.
Empowering Human Oversight
A final consideration in the advancement of FMs is the need for robust human oversight. While FMs have proven their ability to derive insights at an unprecedented scale, human expertise and intuition remain invaluable, particularly in high-stakes decision-making scenarios.
One innovative solution involves designing hybrid models that combine the strengths of FMs with a human-in-the-loop approach. These hybrid models enable human experts to interact with the system, provide feedback, and make informed decisions based on both model predictions and their own judgment. This symbiotic relationship between AI and human intelligence fosters a more accountable, explainable, and reliable decision-making process.
In conclusion, while foundation models have undoubtedly transformed the landscape of deep learning, they come with their own unique challenges. By embracing innovative solutions such as Explainable AI, federated learning, bias mitigation techniques, and human-in-the-loop approaches, we can address these challenges head-on and pave the way for a future where AI is transparent, privacy-conscious, fair, and human-centric.
have revolutionized various fields such as natural language processing, computer vision, and speech recognition. FMs, such as BERT, GPT-3, and Vision Transformers, have achieved remarkable success in tasks like language understanding, text generation, image classification, and object detection.
The power of FMs lies in their ability to learn intricate patterns and representations directly from the data, without the need for explicit feature engineering. They employ deep neural networks with millions or even billions of parameters, enabling them to capture complex relationships and dependencies within the input data. This has led to significant breakthroughs in tasks that were previously considered challenging or unsolved.
One of the key advantages of FMs is their capability to transfer knowledge across domains or tasks. Pretraining a FM on a large corpus of unlabeled data allows it to learn general features and linguistic structures. This pretrained model can then be fine-tuned on specific downstream tasks with smaller labeled datasets. This transfer learning approach has proven to be highly effective, reducing the need for large amounts of task-specific annotated data.
Looking ahead, we can expect further advancements in foundation models. One area of focus will be improving their efficiency and scalability. Current FMs are computationally expensive and require substantial computing resources. Researchers are actively exploring techniques such as model distillation, sparse attention mechanisms, and neural architecture search to make FMs more lightweight and accessible.
Another direction for improvement is enhancing the interpretability of FMs. Despite their remarkable performance, FMs often lack transparency in their decision-making process. Understanding how these models arrive at their predictions is crucial for building trust and ensuring fairness. Researchers are investigating techniques such as attention visualization, saliency maps, and explainable AI methods to shed light on the inner workings of FMs.
Furthermore, there is a growing interest in multimodal foundation models that can handle diverse types of data, such as text, images, speech, and videos. Integrating different modalities into a single model could enable more comprehensive understanding and generation of content. This opens up exciting possibilities for applications in areas like multimodal translation, video summarization, and content generation.
In conclusion, foundation models have had a profound impact on deep learning and have become indispensable tools for various tasks. As research progresses, we can anticipate advancements in efficiency, interpretability, and multimodal capabilities. These developments will further expand the range of applications for FMs and continue to push the boundaries of what is possible in the field of deep learning.
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