by jsendak | Apr 22, 2024 | Computer Science
arXiv:2404.12903v1 Announce Type: new
Abstract: Chinese landscape painting is a gem of Chinese cultural and artistic heritage that showcases the splendor of nature through the deep observations and imaginations of its painters. Limited by traditional techniques, these artworks were confined to static imagery in ancient times, leaving the dynamism of landscapes and the subtleties of artistic sentiment to the viewer’s imagination. Recently, emerging text-to-video (T2V) diffusion methods have shown significant promise in video generation, providing hope for the creation of dynamic Chinese landscape paintings. However, challenges such as the lack of specific datasets, the intricacy of artistic styles, and the creation of extensive, high-quality videos pose difficulties for these models in generating Chinese landscape painting videos. In this paper, we propose CLV-HD (Chinese Landscape Video-High Definition), a novel T2V dataset for Chinese landscape painting videos, and ConCLVD (Controllable Chinese Landscape Video Diffusion), a T2V model that utilizes Stable Diffusion. Specifically, we present a motion module featuring a dual attention mechanism to capture the dynamic transformations of landscape imageries, alongside a noise adapter to leverage unsupervised contrastive learning in the latent space. Following the generation of keyframes, we employ optical flow for frame interpolation to enhance video smoothness. Our method not only retains the essence of the landscape painting imageries but also achieves dynamic transitions, significantly advancing the field of artistic video generation. The source code and dataset are available at https://anonymous.4open.science/r/ConCLVD-EFE3.
Analysis of the Content: Chinese Landscape Painting Videos
This article discusses the creation of dynamic Chinese landscape painting videos using text-to-video (T2V) diffusion methods. It highlights the limitations of traditional techniques that confined these artworks to static imagery, and the potential of T2V methods to bring them to life. The article introduces CLV-HD, a novel T2V dataset for Chinese landscape painting videos, and ConCLVD, a T2V model that utilizes Stable Diffusion. It also presents a motion module with a dual attention mechanism and a noise adapter to capture dynamic transformations and enhance video smoothness.
Multi-disciplinary Nature and Relation to Multimedia Information Systems
The creation of dynamic Chinese landscape painting videos involves a multi-disciplinary approach. It combines elements of art, technology, and computer science to generate videos that showcase the beauty and dynamism of Chinese landscapes. This multi-disciplinary nature is closely related to the field of multimedia information systems.
Multimedia information systems involve the storage, retrieval, and manipulation of different types of media, such as text, images, audio, and video. The T2V methods and techniques discussed in this article are a prime example of how multimedia information systems can be applied to generate dynamic videos from static imagery. By leveraging text, algorithms, and artistic techniques, these systems enhance the user experience and provide new ways of interacting with visual content.
Connection to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concept of creating dynamic Chinese landscape painting videos through T2V methods has a direct connection to animations and virtual realities. Animations involve the manipulation of static images to create the illusion of motion. The T2V techniques described in the article take this concept a step further by generating videos that simulate the experience of exploring a Chinese landscape painting in motion.
Artificial reality, which encompasses augmented reality and virtual reality, also relates to the content of this article. Augmented reality overlays digital content onto the real world, while virtual reality provides immersive experiences in entirely virtual environments. The creation of dynamic Chinese landscape painting videos can be seen as a form of augmented reality, where the videos add a layer of dynamic content to static paintings. These videos can also be part of virtual reality experiences, where users can explore and interact with virtual landscapes inspired by Chinese art.
Expert Insights: Advancements and Challenges
The advancements discussed in this article, such as CLV-HD and ConCLVD, show promising progress in the field of artistic video generation. These techniques enable the creation of dynamic Chinese landscape painting videos that capture the essence of the artworks while providing a visually engaging experience.
However, there are still challenges to overcome. One major difficulty is the lack of specific datasets for Chinese landscape painting videos. Creating a comprehensive and diverse dataset that accurately represents the intricacies of Chinese artistic styles is crucial for training and evaluating T2V models. It requires collaboration between artists, researchers, and experts in cultural heritage.
Another challenge lies in the creation of high-quality videos. Generating high-resolution videos that maintain the fidelity of the original artworks requires advanced algorithms and computational resources. Finding the right balance between preserving artistic sentiment and achieving dynamic transitions is an ongoing area of research.
Despite these challenges, the advancements in T2V methods and the creation of dynamic Chinese landscape painting videos open up possibilities for further exploration. Integrating other forms of media, such as audio and interactive elements, could enhance the immersive experience and provide even more engaging interactions with these artistic representations.
In conclusion, the creation of dynamic Chinese landscape painting videos using T2V methods represents a significant advancement in the field of artistic video generation. This multi-disciplinary approach connects with the wider field of multimedia information systems and relates to concepts like animations, artificial reality, augmented reality, and virtual realities. While challenges exist, further advancements and collaborations have the potential to revolutionize the way we experience and preserve cultural heritage.
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by jsendak | Apr 22, 2024 | Computer Science
Expert Commentary
Online social media platforms have become an integral part of our lives, with users spending hours on these platforms every day. This has provided a wealth of data that can be analyzed to gain insights into public sentiments and mental health. Identifying individuals who may be at risk of suicide early on can potentially save lives. However, traditional techniques for analyzing such large-scale datasets have become ineffective.
This paper proposes a new methodology based on a big data architecture to predict suicidal ideation from social media content. The approach involves two phases: batch processing and real-time streaming prediction. The batch dataset is collected from the Reddit forum and used for model building and training, while the streaming data is extracted using the Twitter streaming API for real-time prediction.
The first phase, batch processing, involves preprocessing the raw data and extracting features. These features are then used to train multiple Apache Spark ML classifiers, including Naive Bayes, Logistic Regression, Linear SVM, Decision Trees, Random Forest, and Multilayer Perceptron. Various feature-extraction techniques are explored, and different testing scenarios are used to evaluate performance.
The experimental results of the batch processing phase indicate that the (Unigram + Bigram) + CV-IDF features with the MLP classifier achieved a high accuracy of 93.47% in classifying suicidal ideation. These features are then applied to the real-time streaming prediction phase.
This research is significant as it takes advantage of big data architecture and machine learning techniques to tackle the challenge of analyzing large-scale social media data for suicide ideation detection. The use of Apache Spark ML classifiers allows for efficient processing of the data and the extraction of meaningful features.
However, there are some limitations to consider. The study only focuses on data from Reddit and Twitter, which may not be representative of all social media platforms. Additionally, the proposed approach assumes that users explicitly express their suicidal thoughts on these platforms, which may not always be the case. Future research could explore incorporating additional data sources and investigating more advanced natural language processing techniques to improve the accuracy of suicide ideation prediction.
In conclusion, this research provides a practical and effective approach for predicting suicidal ideation using social media data. The use of big data architecture and machine learning classifiers allows for efficient processing and accurate prediction. With further refinement and expansion, this methodology could have significant implications for public health and suicide prevention efforts.
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by jsendak | Apr 19, 2024 | Computer Science
arXiv:2404.11938v1 Announce Type: new
Abstract: Multimodal Sentiment Analysis (MSA) aims to identify speakers’ sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multimodal data, such as voiceprints and facial images. Recent distributed collaborative learning has been verified as an effective paradigm for privacy preservation in multimodal tasks. However, they often overlook the privacy distinctions among different modalities, struggling to strike a balance between performance and privacy preservation. Consequently, it poses an intriguing question of maximizing multimodal utilization to improve performance while simultaneously protecting necessary modalities. This paper forms the first attempt at modality-specified (i.e., audio and visual) privacy preservation in MSA tasks. We propose a novel Hybrid Distributed cross-modality cGAN framework (HyDiscGAN), which learns multimodality alignment to generate fake audio and visual features conditioned on shareable de-identified textual data. The objective is to leverage the fake features to approximate real audio and visual content to guarantee privacy preservation while effectively enhancing performance. Extensive experiments show that compared with the state-of-the-art MSA model, HyDiscGAN can achieve superior or competitive performance while preserving privacy.
Multimodal Sentiment Analysis and Privacy Preservation
In the field of multimedia information systems, Multimodal Sentiment Analysis (MSA) has gained significant attention. It involves the analysis of multimodal data, such as audio, visual, and textual information, to identify the sentiment tendencies of speakers in video content. However, the use of multimodal data raises privacy concerns, particularly with the use of voiceprints and facial images.
One approach that has shown promise in preserving privacy in multimodal tasks is distributed collaborative learning. This paradigm allows for learning models to be trained across multiple devices without exchanging sensitive data. However, existing distributed collaborative learning methods often overlook the privacy distinctions among different modalities, leading to a trade-off between performance and privacy preservation.
This paper introduces a novel approach called the Hybrid Distributed cross-modality cGAN framework (HyDiscGAN) to address the privacy concerns in MSA tasks. Unlike previous methods, HyDiscGAN considers the privacy preservation of each modality separately, specifically audio and visual data. By leveraging the fake audio and visual features generated by the framework, HyDiscGAN approximates the real content while preserving privacy.
The core objective of HyDiscGAN is to strike a balance between performance enhancement and privacy preservation. By using shareable, de-identified textual data, the framework learns to generate fake audio and visual features that align with the original content. This approach guarantees privacy preservation while still achieving competitive or superior performance compared to existing state-of-the-art MSA models.
As a multi-disciplinary concept, the research presented in this paper combines aspects of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The use of multimodal data in MSA tasks touches upon various multimedia technologies and techniques, ranging from audio and visual processing to natural language processing and machine learning.
The HyDiscGAN framework not only showcases the potential of distributed collaborative learning in privacy preservation but also offers insights into the future development of MSA models. The modality-specified privacy preservation approach can be extended to other multimodal tasks, allowing for improved performance and privacy protection across different applications.
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by jsendak | Apr 19, 2024 | Computer Science
Analysis: Structured Neuron-level Pruning for Vision Transformers
The article discusses the challenges faced by Vision Transformers (ViTs) in terms of computational cost and memory footprint, which make it difficult to deploy them on devices with limited resources. While conventional pruning approaches can compress and accelerate the Multi-head self-attention (MSA) module in ViTs, they do not take into account the structure of the MSA module.
In response to this, the proposed method, Structured Neuron-level Pruning (SNP), is introduced. SNP aims to prune neurons with less informative attention scores and eliminate redundancy among heads. This is achieved by pruning graphically connected query and key layers with the least informative attention scores, while preserving the overall attention scores. Value layers, on the other hand, can be pruned independently to reduce inter-head redundancy.
The results of applying SNP to Transformer-based models are promising. For example, the DeiT-Small model with SNP runs 3.1 times faster than the original model while achieving 21.94% faster performance and 1.12% higher accuracy than the DeiT-Tiny model. Additionally, SNP can be combined with conventional head or block pruning approaches, resulting in significant parameter and computational cost reduction and faster inference speeds on different hardware platforms.
Overall, SNP presents a novel approach to compressing and accelerating Vision Transformers by considering the structure of the MSA module. By selectively pruning neurons and eliminating redundancy, SNP offers a promising solution to make ViTs more suitable for deployment on edge devices with limited resources, as well as improving performance on server processors.
Expert Insights:
As an expert in the field, I find the proposed SNP method to be a valuable contribution to the optimization of Vision Transformers. The use of structured neuron-level pruning, which takes into account the graph connections within the MSA module, helps to identify and remove redundant information while preserving overall attention scores. This not only leads to significant computational cost reduction but also improves inference speed without sacrificing performance.
The results presented, such as the 3.1 times faster inference speed of DeiT-Small with SNP compared to the original model, demonstrate the effectiveness of the proposed method. Moreover, the successful combination of SNP with head or block pruning approaches further highlights its versatility and potential for even greater compression and speed improvements.
With the increasing demand for deploying vision models on edge devices and the need for efficient use of server processors, techniques like SNP are crucial for making Vision Transformers more practical and accessible. The ability to compress and accelerate such models without compromising their performance opens up new possibilities for a wide range of applications, including real-time computer vision tasks and resource-constrained scenarios.
I believe that the SNP method has the potential to inspire further research in pruning techniques for Vision Transformers, which can lead to the development of more optimized and efficient models. Additionally, future work could explore the application of SNP to other attention-based models or investigate the impact of different pruning strategies on specific vision tasks to identify the most effective combinations.
Overall, the proposed SNP method addresses the challenges of computational cost and memory footprint in Vision Transformers by leveraging structured neuron-level pruning. This approach shows promising results in terms of speed improvement and parameter reduction, making ViTs more suitable for deployment on resource-constrained devices while maintaining or even enhancing performance.
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by jsendak | Apr 18, 2024 | Computer Science
arXiv:2404.10838v1 Announce Type: cross
Abstract: In recent years, pre-trained multimodal large models have attracted widespread attention due to their outstanding performance in various multimodal applications. Nonetheless, the extensive computational resources and vast datasets required for their training present significant hurdles for deployment in environments with limited computational resources. To address this challenge, we propose a novel dynamic self-adaptive multiscale distillation from pre-trained multimodal large model for efficient cross-modal representation learning for the first time. Unlike existing distillation methods, our strategy employs a multiscale perspective, enabling the extraction structural knowledge across from the pre-trained multimodal large model. Ensuring that the student model inherits a comprehensive and nuanced understanding of the teacher knowledge. To optimize each distillation loss in a balanced and efficient manner, we propose a dynamic self-adaptive distillation loss balancer, a novel component eliminating the need for manual loss weight adjustments and dynamically balances each loss item during the distillation process. Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information, requiring minimal computational resources. This efficient approach is suited for various applications and allows the deployment of advanced multimodal technologies even in resource-limited settings. Extensive experiments has demonstrated that our method maintains high performance while significantly reducing model complexity and training costs. Moreover, our distilled student model utilizes only image-level information to achieve state-of-the-art performance on cross-modal retrieval tasks, surpassing previous methods that relied on region-level information.
Analysis of the Content:
The content of this article focuses on the development of a novel approach to address the challenges of deploying pre-trained multimodal large models in resource-limited environments. The authors propose a dynamic self-adaptive multiscale distillation method that allows for efficient cross-modal representation learning.
One key aspect of this method is the use of a multiscale perspective, which enables the extraction of structural knowledge from the pre-trained multimodal large model. This means that the student model, which is the model being trained, inherits a comprehensive and nuanced understanding of the teacher knowledge. This is crucial for ensuring that the student model maintains high performance.
To optimize the distillation process, the authors propose a dynamic self-adaptive distillation loss balancer. This component eliminates the need for manual loss weight adjustments and dynamically balances each loss item during the distillation process. This not only streamlines the training process but also reduces the computational resources required.
The article highlights that this approach is well-suited for various applications and allows for the deployment of advanced multimodal technologies even in resource-limited settings. This is particularly relevant in fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, where computational resources can be a limiting factor.
The authors also mention that their approach achieves state-of-the-art performance on cross-modal retrieval tasks using only image-level information. This is notable because previous methods relied on region-level information, which requires more computational resources.
Expert Insights:
The proposed approach in this article is highly significant for the field of multimedia information systems and related areas such as animations, artificial reality, augmented reality, and virtual realities. These fields often involve the processing and analysis of multimodal data, such as images and text, and require efficient representation learning methods.
The multiscale perspective employed in this approach is particularly interesting from a multidisciplinary standpoint. It combines concepts from computer vision, natural language processing, and knowledge distillation to enhance the learning process. This integration of different disciplines allows for a more comprehensive understanding of the data and improves the performance of the trained models.
The dynamic self-adaptive distillation loss balancer is another innovative component of this approach. Manual adjustments of loss weights can be time-consuming and may not lead to optimal results. By automating this process and dynamically balancing the loss items, the training becomes more efficient and effective. This is crucial in resource-limited environments, where computational resources are scarce.
The findings of this study not only contribute to the field of multimodal representation learning but also have practical implications. The ability to deploy advanced multimodal technologies in resource-limited settings opens up new possibilities for various applications. For example, in the field of augmented reality, where computational resources are often limited on mobile devices, this approach could enable more sophisticated and interactive AR experiences.
Overall, this article provides valuable insights into the development of efficient cross-modal representation learning methods and their applicability in multimedia information systems and related fields. The combination of the multiscale perspective and dynamic self-adaptive distillation loss balancer makes this approach highly promising for future research and practical implementations.
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by jsendak | Apr 18, 2024 | Computer Science
Expert Commentary: Fine Tuning LLMs for Proprietary Domain Knowledge
Large Language Models (LLMs) have become increasingly essential for enterprises to handle complex language tasks. However, one challenge faced by these enterprises is how to imbibe LLMs with domain-specific knowledge efficiently and effectively, while optimizing resources and costs.
An approach often used by enterprises is Retrieval Augmented Generation (RAG), which enhances language models’ capabilities by utilizing vector databases for retrieving information. While this approach doesn’t require fine tuning LLMs explicitly, its effectiveness is limited by the quality and capabilities of the vector databases rather than the inherent potential of the LLMs themselves.
In this article, the focus is on fine tuning LLaMA, an open-source LLM, using proprietary documents and code from an enterprise repository. The goal is to evaluate the quality of responses generated by the fine tuned models. Additionally, this work aims to provide guidance to beginners on how to start with fine tuning LLMs for documentation and code.
One of the crucial considerations when fine tuning LLMs is the choice of GPU size required. The article suggests making educated guesses to determine the appropriate GPU size for optimal performance. Choosing the right GPU size is crucial to ensure efficient training and inference during the fine tuning process.
The article also proposes pre-processing recipes for both document and code datasets. These recipes help in formatting the data into different formats to facilitate the fine tuning process. For document datasets, the suggested methods include forming paragraph chunks, question and answer pairs, and keyword and paragraph chunk pairs. On the other hand, for code datasets, the recommendation is to form summary and function pairs.
Furthermore, the article provides a qualitative evaluation of the fine tuned models’ results for domain-specific queries. This evaluation helps in assessing the models’ performance and their ability to generate relevant and accurate responses based on the domain-specific knowledge they have acquired through fine tuning.
In conclusion, this article offers practical guidelines and recommendations for enterprises looking to fine tune LLMs for proprietary domain knowledge. By leveraging the techniques discussed, enterprises can enhance the capabilities of LLMs and enable them to provide more accurate and contextually appropriate responses, ultimately improving their language processing tasks.
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