by jsendak | Sep 5, 2024 | Computer Science
Expert Commentary: Deep Spiking Neural Networks and Energy Efficiency
In this article, the authors discuss the importance of energy efficiency in deep learning models and explore the potential of spiking neural networks (SNNs) as an energy-efficient alternative. SNNs are inspired by the human brain and utilize event-driven spikes for computation, offering the promise of reduced energy consumption.
The article provides an overview of the existing methods for developing deep SNNs, focusing on two main approaches: (1) ANN-to-SNN conversion, and (2) direct training with surrogate gradients. ANN-to-SNN conversion involves transforming a pre-trained artificial neural network (ANN) into an SNN, enabling the use of existing ANN architectures. Direct training with surrogate gradients, on the other hand, allows for the training of SNNs from scratch using gradient-based optimization algorithms.
Additionally, the authors categorize the network architectures for deep SNNs into deep convolutional neural networks (DCNNs) and Transformer architecture. DCNNs have shown success in computer vision tasks, while Transformer architecture has revolutionized natural language processing tasks. The exploration of these architectures in the context of SNNs opens up exciting possibilities for energy-efficient deep learning across various domains.
A significant contribution of this article is the comprehensive comparison of state-of-the-art deep SNNs, with a particular emphasis on emerging Spiking Transformers. Spiking Transformers combine the strengths of the Transformer architecture with the energy efficiency of SNNs, making them a promising avenue for future research.
Looking ahead, the authors outline future directions for building large-scale SNNs. They highlight the need for advancements in hardware design to support the efficient execution of SNN models. Additionally, they emphasize the importance of developing efficient learning algorithms that leverage the unique properties of SNNs.
Overall, this article sheds light on the potential of spiking neural networks as energy-efficient alternatives to traditional deep learning models. It provides a valuable survey of existing methods and architectures for deep SNNs and identifies the emerging trend of Spiking Transformers. The outlined future directions provide a roadmap for researchers and practitioners to further explore and develop large-scale SNNs.
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by jsendak | Sep 4, 2024 | Computer Science
arXiv:2409.00022v1 Announce Type: new
Abstract: The landscape of social media content has evolved significantly, extending from text to multimodal formats. This evolution presents a significant challenge in combating misinformation. Previous research has primarily focused on single modalities or text-image combinations, leaving a gap in detecting multimodal misinformation. While the concept of entity consistency holds promise in detecting multimodal misinformation, simplifying the representation to a scalar value overlooks the inherent complexities of high-dimensional representations across different modalities. To address these limitations, we propose a Multimedia Misinformation Detection (MultiMD) framework for detecting misinformation from video content by leveraging cross-modal entity consistency. The proposed dual learning approach allows for not only enhancing misinformation detection performance but also improving representation learning of entity consistency across different modalities. Our results demonstrate that MultiMD outperforms state-of-the-art baseline models and underscore the importance of each modality in misinformation detection. Our research provides novel methodological and technical insights into multimodal misinformation detection.
Expert Commentary:
This article explores the challenge of combating misinformation in the evolving landscape of social media content, which has extended from text to multimodal formats. While previous research has primarily focused on single modalities or text-image combinations, there is a gap in detecting multimodal misinformation. This is where the proposed Multimedia Misinformation Detection (MultiMD) framework comes into play.
The MultiMD framework aims to address the limitations of existing methods by leveraging cross-modal entity consistency in video content to detect misinformation. The framework takes a dual learning approach, which not only enhances misinformation detection performance but also improves representation learning of entity consistency across different modalities.
One of the key aspects of this framework is its multi-disciplinary nature. It combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By leveraging the inherent complexities of high-dimensional representations across different modalities, MultiMD is able to provide more accurate and robust detection of multimodal misinformation.
The results of the study demonstrate the effectiveness of the MultiMD framework, as it outperforms state-of-the-art baseline models in detecting misinformation. This reinforces the importance of considering each modality when detecting and combating misinformation in multimedia content.
In the wider field of multimedia information systems, this research contributes novel methodological and technical insights into multimodal misinformation detection. It highlights the need for more comprehensive approaches that take into account the diverse range of content formats present in social media platforms.
Overall, the MultiMD framework has the potential to significantly advance the field of misinformation detection by providing a more holistic and accurate approach to combatting multimodal misinformation. As the landscape of social media content continues to evolve, it is crucial to develop robust techniques that can effectively detect and mitigate the spread of misinformation in various modalities.
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by jsendak | Sep 4, 2024 | Computer Science
Expert Commentary: Evaluating the Reliability of Explainable AI in Predicting Cerebral Palsy
This study explores the potential of Explainable AI (XAI) methods in predicting Cerebral Palsy (CP) by analyzing skeletal data extracted from video recordings of infant movements. Early detection of CP is crucial for effective intervention and monitoring, making this research significant for improving diagnosis and treatment outcomes.
One of the main challenges in using deep learning models for medical applications is the lack of interpretability. XAI aims to address this issue by providing explanations of the model’s decision-making process, enabling medical professionals to understand and trust the predictions.
In this study, the authors employ two XAI methods, namely Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), to detect key body points influencing CP predictions. They utilize a unique dataset of infant movements and apply skeleton data perturbations to evaluate the reliability and applicability of these XAI methods.
The evaluation metrics used in this study are faithfulness and stability. Faithfulness measures the extent to which the XAI method’s explanations align with the model’s actual decision criteria. Stability, on the other hand, evaluates the robustness of the explanations against minor data perturbations.
The results indicate that both CAM and Grad-CAM effectively identify key body points influencing CP predictions. However, the performance differs in terms of specific metrics. Grad-CAM outperforms CAM in terms of stability, particularly in measuring velocity (RISv). This indicates that Grad-CAM’s explanations remain consistent even when there are slight fluctuations in the data. On the other hand, CAM performs better in measuring bone stability (RISb) and internal representation robustness (RRS).
Another interesting finding of this study is the evaluation of the XAI metrics for both the overall ensemble and the individual models within the ensemble. The ensemble approach provides a representation of outcomes from its constituent models, demonstrating the potential for combining multiple models to improve prediction accuracy and interpretability.
It is worth noting that the individual models within the ensemble show varied results, and neither CAM nor Grad-CAM consistently outperform the other. This suggests that the ensemble approach leverages the diversity of the constituent models to provide a more comprehensive understanding of the prediction process.
Overall, this study demonstrates the reliability and applicability of XAI methods, specifically CAM and Grad-CAM, in predicting CP using skeletal data extracted from video recordings of infant movements. The findings contribute to the field of medical AI, showing the potential for XAI to improve the interpretability and trustworthiness of deep learning models in healthcare applications.
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by jsendak | Sep 2, 2024 | Computer Science
arXiv:2408.16990v1 Announce Type: new
Abstract: Adding proper background music helps complete a short video to be shared. Towards automating the task, previous research focuses on video-to-music retrieval (VMR), aiming to find amidst a collection of music the one best matching the content of a given video. Since music tracks are typically much longer than short videos, meaning the returned music has to be cut to a shorter moment, there is a clear gap between the practical need and VMR. In order to bridge the gap, we propose in this paper video to music moment retrieval (VMMR) as a new task. To tackle the new task, we build a comprehensive dataset Ad-Moment which contains 50K short videos annotated with music moments and develop a two-stage approach. In particular, given a test video, the most similar music is retrieved from a given collection. Then, a Transformer based music moment localization is performed. We term this approach Retrieval and Localization (ReaL). Extensive experiments on real-world datasets verify the effectiveness of the proposed method for VMMR.
Automating Video to Music Moment Retrieval: Bridging the Gap
In the field of multimedia information systems, the integration of audio and visual elements is crucial for creating immersive experiences. One key aspect of this integration is the synchronization of background music with video content. Adding proper background music not only enhances the emotional impact of a video but also helps to engage and captivate the audience.
Previous research has primarily focused on video-to-music retrieval (VMR), which aims to find the best-matching music track for a given video from a collection of music tracks. However, a significant gap exists between the practical need, where short videos need to be matched with shorter music moments, and the capabilities of VMR systems.
Addressing this gap, the authors propose a new task called video to music moment retrieval (VMMR). This task involves retrieving the most similar music moment from a given collection for a given test video. To support the development and evaluation of VMMR algorithms, the authors introduce the Ad-Moment dataset, which includes 50,000 short videos annotated with music moments.
The authors propose a two-stage approach, named Retrieval and Localization (ReaL), to tackle the VMMR task. In the first stage, the most similar music track is retrieved from the collection using a similarity measure. In the second stage, a Transformer-based model is employed to perform music moment localization, i.e., identifying the specific portion of the retrieved music track that best matches the video.
Multiple disciplines intersect in this research, highlighting the multi-disciplinary nature of multimedia information systems. The study combines concepts from computer vision, audio processing, machine learning, and artificial intelligence to automate the process of video to music moment retrieval.
Furthermore, the proposed method has implications for other areas such as animations, artificial reality, augmented reality, and virtual realities. These fields often rely on multimedia content to create immersive and engaging experiences. By automating the process of matching music moments with video content, the proposed method can enhance the creation of animations, improve the realism of artificial and augmented reality environments, and enrich the immersion of virtual reality experiences.
The effectiveness of the proposed method for VMMR is verified through extensive experiments on real-world datasets. The results demonstrate the potential of the approach to bridge the gap between practical needs and existing VMR capabilities. As future work, further refinements and optimizations of the ReaL approach could be explored, such as incorporating user preferences, evaluating the impact of different music genres on video engagement, and exploring novel methods for music moment retrieval and localization.
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by jsendak | Sep 2, 2024 | Computer Science
As Generative Artificial Intelligence (GenAI) technologies continue to advance rapidly, the issue of governance and regulation has emerged as a critical challenge. The development and implementation of governance approaches for GenAI have not kept pace with the technology itself, leading to discrepancies and a lack of consistent provisions across different regions globally.
In order to address this challenge, the authors of this paper have proposed a Harmonized GenAI Framework, or “H-GenAIGF,” which aims to provide a collective view of different governance approaches from six key regions: the European Union (EU), United States (US), China (CN), Canada (CA), United Kingdom (UK), and Singapore (SG). By analyzing the governance approaches of these regions, the authors have identified four key constituents, fifteen processes, twenty-five sub-processes, and nine principles that contribute to the effective governance of GenAI.
Furthermore, the paper includes a comparative analysis of these governance approaches, aiming to identify commonalities and distinctions between regions in terms of process coverage. The results of this analysis reveal that risk-based approaches tend to provide the most comprehensive coverage of processes, followed by mixed approaches. On the other hand, other approaches fall short, covering less than half of the processes identified.
An important finding from this research is that only one process aligns across all governance approaches from the different regions. This highlights the lack of consistent and executable provisions for GenAI governance. To support this finding, the authors also conducted a case study on ChatGPT, a popular AI model, and found a deficiency in process coverage. This further emphasizes the need for harmonization of governance approaches to ensure alignment and effectiveness in GenAI governance.
In conclusion, this paper provides valuable insights into the current state of GenAI governance globally. The proposed Harmonized GenAI Framework offers a comprehensive perspective by identifying key constituents, processes, sub-processes, and principles. The comparative analysis highlights the discrepancies and convergences between regions, emphasizing the need for consistent and executable provisions. Moving forward, it is crucial for global governance to keep pace with GenAI technologies, addressing the identified limitations and fostering safe and trustworthy adoption of this powerful technology.
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by jsendak | Aug 30, 2024 | Computer Science
arXiv:2408.16564v1 Announce Type: new
Abstract: Humans naturally perform audiovisual speech recognition (AVSR), enhancing the accuracy and robustness by integrating auditory and visual information. Spiking neural networks (SNNs), which mimic the brain’s information-processing mechanisms, are well-suited for emulating the human capability of AVSR. Despite their potential, research on SNNs for AVSR is scarce, with most existing audio-visual multimodal methods focused on object or digit recognition. These models simply integrate features from both modalities, neglecting their unique characteristics and interactions. Additionally, they often rely on future information for current processing, which increases recognition latency and limits real-time applicability. Inspired by human speech perception, this paper proposes a novel human-inspired SNN named HI-AVSNN for AVSR, incorporating three key characteristics: cueing interaction, causal processing and spike activity. For cueing interaction, we propose a visual-cued auditory attention module (VCA2M) that leverages visual cues to guide attention to auditory features. We achieve causal processing by aligning the SNN’s temporal dimension with that of visual and auditory features and applying temporal masking to utilize only past and current information. To implement spike activity, in addition to using SNNs, we leverage the event camera to capture lip movement as spikes, mimicking the human retina and providing efficient visual data. We evaluate HI-AVSNN on an audiovisual speech recognition dataset combining the DVS-Lip dataset with its corresponding audio samples. Experimental results demonstrate the superiority of our proposed fusion method, outperforming existing audio-visual SNN fusion methods and achieving a 2.27% improvement in accuracy over the only existing SNN-based AVSR method.
Expert Commentary: The Potential of Spiking Neural Networks for Audiovisual Speech Recognition
Audiovisual speech recognition (AVSR) is a fascinating area of research that aims to integrate auditory and visual information to enhance the accuracy and robustness of speech recognition systems. In this paper, the researchers focus on the potential of spiking neural networks (SNNs) as an effective model for AVSR. As a commentator with expertise in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, I find this study highly relevant and interesting.
One of the key contributions of this paper is the development of a human-inspired SNN called HI-AVSNN. By mimicking the brain’s information-processing mechanisms, SNNs have the advantage of capturing the temporal dynamics of audiovisual speech signals. This is crucial for accurate AVSR, as speech communication involves complex interactions between auditory and visual modalities.
The authors propose three key characteristics for their HI-AVSNN model: cueing interaction, causal processing, and spike activity. Cueing interaction refers to the use of visual cues to guide attention to auditory features. This is inspired by how humans naturally focus their attention on relevant visual information during speech perception. By incorporating cueing interaction into their model, the researchers aim to improve the fusion of auditory and visual information.
Causal processing is another important characteristic of the HI-AVSNN model. By aligning the temporal dimension of the SNN with that of visual and auditory features, and utilizing only past and current information through temporal masking, the model can operate in a causal manner. This is essential for real-time applicability, as relying on future information would increase recognition latency.
The third characteristic, spike activity, is implemented by leveraging the event camera to capture lip movement as spikes. This approach mimics the human retina, which is highly efficient in processing visual data. By incorporating the event camera and SNNs, the model can effectively process visual cues and achieve efficient AVSR.
From a multi-disciplinary perspective, this study combines concepts from neuroscience, computer vision, and artificial intelligence. The integration of auditory and visual modalities requires a deep understanding of human perception, the analysis of audiovisual signals, and the development of advanced machine learning models. The authors successfully bridge these disciplines to propose an innovative approach for AVSR.
In the wider field of multimedia information systems, including animations, artificial reality, augmented reality, and virtual realities, AVSR plays a crucial role. Accurate recognition of audiovisual speech is essential for applications such as automatic speech recognition, video conferencing, virtual reality communication, and human-computer interaction. The development of a robust and efficient AVSR system based on SNNs could greatly enhance these applications and provide a more immersive and natural user experience.
In conclusion, the paper presents a compelling case for the potential of spiking neural networks in audiovisual speech recognition. The HI-AVSNN model incorporates important characteristics inspired by human speech perception and outperforms existing methods in terms of accuracy. As further research and development in this area continue, we can expect to see advancements in multimedia information systems and the integration of audiovisual modalities in various applications.
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