by jsendak | Apr 26, 2024 | Computer Science
arXiv:2404.16305v1 Announce Type: new
Abstract: Existing works have made strides in video generation, but the lack of sound effects (SFX) and background music (BGM) hinders a complete and immersive viewer experience. We introduce a novel semantically consistent v ideo-to-audio generation framework, namely SVA, which automatically generates audio semantically consistent with the given video content. The framework harnesses the power of multimodal large language model (MLLM) to understand video semantics from a key frame and generate creative audio schemes, which are then utilized as prompts for text-to-audio models, resulting in video-to-audio generation with natural language as an interface. We show the satisfactory performance of SVA through case study and discuss the limitations along with the future research direction. The project page is available at https://huiz-a.github.io/audio4video.github.io/.
Improving the Immersive Experience with Video-to-Audio Generation
In the field of multimedia information systems, the combination of audio and visual elements plays a crucial role in creating an immersive viewer experience. While existing works have made significant strides in video generation, there has been a lack of attention to the inclusion of sound effects (SFX) and background music (BGM) in the generated videos. This omission hinders the creation of a complete and truly immersive viewer experience.
To address this limitation, a novel framework called SVA (Semantically-consistent Video-to-Audio generation) has been introduced. The primary objective of SVA is to automatically generate audio that is semantically consistent with the given video content. By harnessing the power of multimodal large language models (MLLM), SVA is able to understand the semantics of a video from its key frame and generate creative audio schemes that correspond to it.
The use of multimodal language models is significant in highlighting the multi-disciplinary nature of this research. It brings together concepts from natural language processing, computer vision, and audio processing to create an integrated framework that addresses a gap in the existing video generation techniques.
SVA makes use of prompts generated by the MLLM to drive text-to-audio models. These text-to-audio models then generate the final audio that is synchronized with the video content. The natural language interface provided by the prompts allows for intuitive control over the audio generation process.
The successful implementation of SVA has been demonstrated through a case study, which showcases the satisfactory performance of the framework. By generating audio that is semantically consistent with the video, SVA enhances the overall viewer experience, making it more immersive and engaging.
Looking ahead, the limitations and future research directions of the SVA framework need to be explored. For instance, how can the generation of audio be further enhanced to capture more fine-grained details of the video content? Additionally, the integration of SVA with emerging technologies such as augmented reality (AR) and virtual reality (VR) could open up new possibilities for creating highly immersive multimedia experiences.
In conclusion, the introduction of the SVA framework represents a significant advancement in the field of multimedia information systems. By automatically generating semantically consistent audio for videos, SVA contributes to the creation of more immersive and engaging viewer experiences. Its multi-disciplinary nature, combining concepts from natural language processing, computer vision, and audio processing, highlights the importance of integrating multiple domains for the advancement of multimedia technologies.
You can learn more about the SVA framework and access the project page here.
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by jsendak | Apr 26, 2024 | Computer Science
In this article, the authors introduce VN-Net, a new approach that combines spatio-temporal graph convolutional networks (ST-GCNs) with vision data from satellites for sparse meteorological forecasting. While previous studies have demonstrated the effectiveness of ST-GCNs in predicting numerical data from ground weather stations, the authors explore the untapped potential of using satellite imagery as high fidelity and low latency data.
VN-Net consists of two main components: Numerical-GCN (N-GCN) and Vision-LSTM Network (V-LSTM). N-GCN is responsible for modeling the static and dynamic patterns of spatio-temporal numerical data, while V-LSTM captures multi-scale joint channel and spatial features from time series satellite images. The authors also develop a GCN-based decoder that generates hourly predictions of specific meteorological factors.
This approach is the first of its kind, as no previous studies have integrated GCN methods with multi-modal data for sparse spatio-temporal meteorological forecasting. To evaluate VN-Net, the authors conducted experiments on the Weather2k dataset and compared the results with state-of-the-art methods. The results demonstrate that VN-Net outperforms existing approaches by a significant margin in terms of mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and visibility forecasting.
In addition to the quantitative evaluation, the authors also perform interpretation analysis to gain insights into the impact of incorporating vision data. This analysis helps validate the effectiveness of using satellite imagery in improving meteorological forecasting accuracy.
Overall, this research opens up new possibilities for enhancing meteorological forecasting by leveraging multi-modal data and advanced machine learning techniques. The integration of vision data from satellites with ST-GCNs provides a promising avenue for fine-grained weather forecasting and warrants further exploration. Future studies could focus on expanding the application of VN-Net to other meteorological factors and datasets, as well as optimizing the model architecture to achieve even better results.
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by jsendak | Apr 24, 2024 | Computer Science
arXiv:2404.14573v1 Announce Type: new
Abstract: A key challenge of 360$^circ$ VR video streaming is ensuring high quality with limited network bandwidth. Currently, most studies focus on tile-based adaptive bitrate streaming to reduce bandwidth consumption, where resources in network nodes are not fully utilized. This article proposes a tile-weighted rate-distortion (TWRD) packet scheduling optimization system to reduce data volume and improve video quality. A multimodal spatial-temporal attention transformer is proposed to predict viewpoint with probability that is used to dynamically weight tiles and corresponding packets. The packet scheduling problem of determining which packets should be dropped is formulated as an optimization problem solved by a dynamic programming solution. Experiment results demonstrate the proposed method outperforms the existing methods under various conditions.
Improving 360° VR Video Streaming with Tile-Weighted Rate-Distortion Packet Scheduling
360° VR video streaming has become increasingly popular, allowing users to immerse themselves in virtual environments. However, a major challenge in this field is ensuring high video quality while using limited network bandwidth. Most current studies focus on tile-based adaptive bitrate streaming, which reduces bandwidth consumption but fails to fully utilize network resources.
This article introduces a novel solution called the Tile-Weighted Rate-Distortion (TWRD) packet scheduling optimization system. The goal is to reduce data volume and enhance video quality in 360° VR streaming. The system utilizes a multimodal spatial-temporal attention transformer to predict the user’s viewpoint. This prediction is then used to dynamically weight tiles and corresponding packets based on their importance to the user’s current view.
One of the main contributions of this study is the formulation of the packet scheduling problem as an optimization task. By using dynamic programming, the system determines which packets should be dropped to achieve the best trade-off between video quality and bandwidth usage. This approach allows for efficient and effective packet scheduling, improving the overall streaming experience.
The results of experiments conducted in this study demonstrate the superiority of the proposed method compared to existing approaches under various conditions. The TWRD packet scheduling optimization system consistently outperforms other methods in terms of video quality and bandwidth utilization.
Multi-disciplinary Nature of the Study
This article combines concepts from multiple disciplines to address the challenges of 360° VR video streaming. The study incorporates techniques from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Within multimedia information systems, the study tackles the issue of efficient video streaming and resource utilization. By introducing the TWRD packet scheduling optimization system, the authors propose a solution that optimizes video quality while minimizing bandwidth consumption.
The incorporation of animations is crucial in 360° VR video streaming, as smooth and realistic movements are essential for an immersive experience. The multimodal spatial-temporal attention transformer used in this study leverages animation techniques to predict the user’s viewpoint and dynamically weight tiles and packets accordingly.
Artificial reality, augmented reality, and virtual realities are closely related to 360° VR video streaming. These fields aim to create lifelike and interactive virtual environments. The TWRD packet scheduling optimization system contributes to these areas by enhancing the quality and realism of VR video streaming.
In conclusion, this article presents a comprehensive solution to the challenges of 360° VR video streaming. By combining techniques from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, the study offers a novel approach to optimizing video quality and bandwidth utilization. The proposed TWRD packet scheduling optimization system has the potential to greatly improve the overall streaming experience for users of 360° VR content.
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by jsendak | Apr 24, 2024 | Computer Science
Expert Commentary:
In this article, the authors present a system designed to remove real-world reflections from consumer photography by leveraging linear (RAW) photos and contextual photos taken from the opposite direction. This approach helps the system distinguish between the actual scene and unwanted reflections.
A noteworthy aspect of this system is that it is trained using synthetic mixtures of real-world RAW images. The reflections in these images are simulated with high accuracy both in terms of photometric and geometric properties. This training approach ensures that the system can effectively handle a wide range of reflection scenarios encountered in consumer photography.
The system comprises a two-stage process. The first stage involves a base model that takes the captured photo and optional contextual photo as input, and processes them at a resolution of 256p. This initial processing allows the system to generate a preliminary output. In the second stage, an up-sampling model is used to transform the 256p images to full resolution, enhancing the details and quality of the final output.
A notable highlight of this system is its efficiency. The authors report that it can produce images for review at 1K resolution in just 6.5 seconds on an iPhone 14 Pro. This rapid processing time makes the system highly practical for real-time usage and improves the overall user experience.
While the article provides promising results, further research could explore the system’s performance on more challenging reflection scenarios, such as complex glass surfaces or highly reflective materials. Additionally, investigating the system’s applicability to non-consumer photography domains, such as professional photography or industrial imaging, would be an interesting direction for future exploration.
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by jsendak | Apr 23, 2024 | Computer Science
arXiv:2404.13134v1 Announce Type: new
Abstract: In this work, we introduce a novel deep learning-based approach to text-in-image watermarking, a method that embeds and extracts textual information within images to enhance data security and integrity. Leveraging the capabilities of deep learning, specifically through the use of Transformer-based architectures for text processing and Vision Transformers for image feature extraction, our method sets new benchmarks in the domain. The proposed method represents the first application of deep learning in text-in-image watermarking that improves adaptivity, allowing the model to intelligently adjust to specific image characteristics and emerging threats. Through testing and evaluation, our method has demonstrated superior robustness compared to traditional watermarking techniques, achieving enhanced imperceptibility that ensures the watermark remains undetectable across various image contents.
Introduction
In this work, the authors present a cutting-edge deep learning-based approach to text-in-image watermarking. This method aims to embed and extract textual information within images to enhance data security and integrity. The authors leverage the capabilities of deep learning, specifically using Transformer-based architectures for text processing and Vision Transformers for image feature extraction.
Deep Learning for Text-in-Image Watermarking
Deep learning has revolutionized various domains, and its potential in multimedia information systems is immense. This work addresses the problem of text-in-image watermarking utilizing deep learning techniques to achieve superior results compared to traditional watermarking methods. By using advanced Transformer-based architectures, the proposed method enables the embedding and extraction of textual information in images while ensuring robustness against emerging threats.
Multimedia information systems encompass a wide range of technologies and techniques, including animations, artificial reality, augmented reality, and virtual realities. The integration of deep learning in text-in-image watermarking adds another layer of complexity to these interdisciplinary fields.
Transformer-based Architectures for Text Processing
The authors utilize Transformer-based architectures for text processing, which have proven to be highly effective in natural language processing tasks. By adapting these models to the context of text-in-image watermarking, they enable the intelligent embedding and extraction of textual information that seamlessly integrates with the image content.
These Transformer-based architectures excel at capturing contextual dependencies within the text, allowing the watermark to be adjusted and adapt to specific image characteristics. This adaptivity is a significant improvement over traditional watermarking techniques, as it ensures the imperceptibility of the watermark across various image contents.
Vision Transformers for Image Feature Extraction
The authors also leverage Vision Transformers, another advanced deep learning architecture specifically designed for image feature extraction. By combining the power of Transformer-based architectures for text processing with Vision Transformers for image analysis, the proposed method achieves state-of-the-art results in text-in-image watermarking.
These Vision Transformers effectively capture the visual features of the images, enabling accurate integration of the textual watermark. The integration of these multi-disciplinary concepts furthers the development of multimedia information systems and opens up new possibilities in the field of text and image processing.
Evaluation and Future Directions
The authors extensively evaluate their proposed method and demonstrate its superiority over traditional watermarking techniques. The enhanced imperceptibility achieved by the deep learning-based approach ensures that the text-in-image watermark remains undetectable across various image contents.
This work represents a significant step forward in the field of multimedia information systems, specifically concerning text-in-image watermarking. The integration of deep learning techniques and cutting-edge architectures paves the way for future developments in multimedia security and data integrity.
Future directions for research in this area could focus on further enhancing the robustness of the proposed method against emerging threats. Additionally, exploring the potential of combining deep learning approaches with augmented reality and virtual reality can lead to novel applications in multimedia information systems.
Conclusion
This article introduces a novel deep learning-based approach to text-in-image watermarking that sets new benchmarks in the field. By leveraging Transformer-based architectures for text processing and Vision Transformers for image feature extraction, the proposed method achieves superior results and enhanced imperceptibility.
The multi-disciplinary nature of the concepts discussed highlights the potential for cross-pollination between different fields, such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Continued research in these areas holds great promise for advancing the capabilities of multimedia systems and ensuring data security and integrity.
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by jsendak | Apr 23, 2024 | Computer Science
Expert Commentary:
In this article, the authors highlight the importance of global placement in VLSI physical design and specifically address the challenges posed by the wide use of 2D processing element (PE) arrays in machine learning accelerators. State-of-the-art academic global placers often struggle with scalability and Quality of Results (QoR) when dealing with these complex designs. To overcome these challenges, the authors propose DG-RePlAce, a new and fast GPU-accelerated global placement framework that leverages the dataflow and datapath structures of machine learning accelerators.
The experimental results presented in this work demonstrate the effectiveness of DG-RePlAce in improving the routed wirelength and total negative slack (TNS) of machine learning accelerators. Compared to the RePlAce (DREAMPlace) approach, DG-RePlAce achieves a reduction in routed wirelength by an average of 10% and total negative slack by 31%, with faster global placement and comparable total runtimes. These results indicate that the proposed framework can effectively optimize the physical design of machine learning accelerators.
Furthermore, the authors also conducted empirical studies on the TILOS MacroPlacement Benchmarks, which showed promising post-route improvements over RePlAce and DREAMPlace. This suggests that DG-RePlAce has the potential to extend beyond machine learning accelerators and be applicable to a wider range of designs.
Overall, the introduction of DG-RePlAce addresses the growing need for efficient and scalable global placement techniques for VLSI physical design, particularly in the context of machine learning accelerators. By leveraging GPU acceleration and taking advantage of the specific structures present in these designs, DG-RePlAce offers significant improvements in terms of wirelength, slack, and runtime. Further research and experimentation could explore the applicability of this approach to other VLSI designs and investigate potential optimizations for even greater QoR gains.
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