by jsendak | Jan 9, 2024 | Computer Science
Law enforcement officials rely heavily on Forensic Video Analytic (FVA) Software in their investigation of crimes. However, the current FVA software available in the market is complex, time-consuming, equipment-dependent, and expensive. This poses a problem for developing countries that struggle to gain access to this crucial technology.
To address these shortcomings, a team embarked on a Final Year Project to develop an efficient and effective FVA software. They conducted a thorough review of scholarly research papers, online databases, and legal documentation to identify the areas that needed improvement. The scope of their project covered multiple aspects of FVA, including object detection, object tracking, anomaly detection, activity recognition, tampering detection, image enhancement, and video synopsis.
To achieve their goals, the team employed various machine learning techniques, GPU acceleration, and efficient architecture development. They used CNN, GMM, multithreading, and OpenCV C++ coding to create their software. By implementing these methods, they aimed to speed up the FVA process, particularly through their innovative research on video synopsis.
The project yielded three significant research outcomes: Moving Object Based Collision-Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture, and Tampering Detection Inter-Frame Forgery. These outcomes were achieved through the integration of efficient algorithms and optimizations to overcome limitations in processing power and memory. The team had to strike a balance between real-time performance and accuracy to ensure the software’s practicality.
Additionally, the research outcomes included forensic and surveillance panel outcomes specifically tailored for the Sri Lankan context. This demonstrates the team’s focus on addressing the needs and challenges faced by law enforcement in their home country.
In conclusion, this Final Year Project successfully developed an efficient and effective FVA software by leveraging machine learning techniques, optimized algorithms, and innovative research on video synopsis. The implications of their work are far-reaching, potentially revolutionizing the way law enforcement agencies handle video evidence. This project serves as a stepping stone towards providing developing countries with better access to the tools and technology needed for effective crime investigation and prevention.
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by jsendak | Jan 9, 2024 | Computer Science
Multimodal Large Language Models (MLLMs) are experiencing rapid growth,
yielding a plethora of noteworthy contributions in recent months. The
prevailing trend involves adopting data-driven methodologies, wherein diverse
instruction-following datasets are collected. However, a prevailing challenge
persists in these approaches, specifically in relation to the limited visual
perception ability, as CLIP-like encoders employed for extracting visual
information from inputs. Though these encoders are pre-trained on billions of
image-text pairs, they still grapple with the information loss dilemma, given
that textual captions only partially capture the contents depicted in images.
To address this limitation, this paper proposes to improve the visual
perception ability of MLLMs through a mixture-of-experts knowledge enhancement
mechanism. Specifically, we introduce a novel method that incorporates
multi-task encoders and visual tools into the existing MLLMs training and
inference pipeline, aiming to provide a more comprehensive and accurate
summarization of visual inputs. Extensive experiments have evaluated its
effectiveness of advancing MLLMs, showcasing improved visual perception
achieved through the integration of visual experts.
Multimodal Large Language Models (MLLMs) have been gaining momentum in recent months, thanks to their ability to generate meaningful content by leveraging both text and visual inputs. However, a significant challenge that researchers face when working with MLLMs is the limited visual perception ability of these models.
The existing approach involves using CLIP-like encoders to extract visual information from inputs. These encoders are pre-trained on billions of image-text pairs but still struggle with information loss due to the partial capture of contents in textual captions.
To overcome this limitation, this paper proposes a novel method that enhances the visual perception ability of MLLMs by incorporating a mixture-of-experts knowledge enhancement mechanism. This approach integrates multi-task encoders and visual tools into the training and inference pipeline of MLLMs, enabling a more comprehensive and accurate summarization of visual inputs.
The significance of this research lies in its multi-disciplinary nature. It combines elements from various domains such as natural language processing, computer vision, and artificial intelligence. By leveraging the strengths of different disciplines, the proposed method aims to improve the overall performance of MLLMs when it comes to understanding and generating content based on visual inputs.
In the wider field of multimedia information systems, this research contributes to bridging the gap between textual and visual information processing. With the integration of visual experts into MLLMs, the models become more adept at understanding and leveraging visual cues, leading to enhanced performance in tasks such as image captioning, visual question answering, and content generation.
Additioally, this work has implications for the advancements in Animations, Artificial Reality, Augmented Reality, and Virtual Realities. With better visual perception ability, MLLMs can play a crucial role in generating realistic animations, improving the user experience in artificial and augmented reality applications, and enabling more immersive virtual reality environments. By training MLLMs to understand and interpret visual inputs effectively, these technologies can benefit from more accurate and context-aware content generation.
In conclusion, the proposed method for enhancing the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism presents a promising avenue for advancing these models. By incorporating multi-task encoders and visual tools, the proposed approach enables MLLMs to have a more comprehensive understanding of visual inputs, thereby improving their performance across various domains including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 9, 2024 | Computer Science
Understanding the Complexity of Volcanic Plumbing Systems
Magmatic processes, which involve the formation, movement, and chemical evolution of magmas, are subjects of extensive investigation in the field of volcanology. Scientists employ a wide range of techniques, including fieldwork, geophysics, geochemistry, and various modeling approaches to uncover the underlying mechanisms behind volcanic eruptions. However, despite significant advancements in our understanding, there remains a lack of consensus regarding models of volcanic plumbing systems.
The complexity arises from the integration of multiple processes that originate from the magma source and extend throughout a network of interconnected magma bodies. This network serves as a conduit, connecting the magma source deep in the mantle or lower crust to the volcano itself. Exploring the behavior and dynamics of this network is crucial for understanding volcanic activity.
In a recent study, researchers have turned to a network approach to investigate the potential mechanisms driving magma pool interaction and transfer across the Earth’s crust. The use of a network framework allows for the exploration of diffusion processes within a dynamic spatial context. Notably, this research highlights the intricate relationship between diffusion and network evolution: as diffusion impacts the structure of the network, the network, in turn, influences the diffusion process.
In the proposed model, nodes represent magma pools, while edges symbolize physical connections between them, such as dykes or veinlets. By incorporating rules derived from rock mechanics and melting processes, scientists aim to capture the fundamental dynamics driving magma transport and interaction within the volcanic plumbing system.
This innovative approach holds promise for shedding light on the emergence of various magmatic products. By simulating how magmas diffuse through the interconnected network of magma bodies, researchers can gain insights into the formation and evolution of different volcanic products observed during eruptions. Through a combination of theoretical modeling and experimental validation, this approach has the potential to provide a more comprehensive understanding of volcanic plumbing systems.
The Way Forward
While the network approach presents a significant step towards unraveling the complexity of magmatic processes, further research is required to refine and validate the model. It will be crucial to incorporate insights from ongoing fieldwork, geophysical surveys, and geochemical analysis to ensure the accuracy and applicability of the network-based framework.
Additionally, expanding the scope of the study to include real-world volcanic systems will allow for a better understanding of how the proposed diffusion and network evolution mechanisms manifest in actual eruptions. The integration of observational data, such as volcanic deformation and gas emissions, will provide valuable constraints for validating the model and improving our understanding of volcanic behavior.
Overall, the network approach to investigating volcanic plumbing systems represents a promising avenue for future research. By combining theoretical models with empirical data and leveraging interdisciplinary collaborations, scientists can continue to advance our understanding of magmatic processes and ultimately enhance volcanic hazard assessment and mitigation efforts.
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by jsendak | Jan 9, 2024 | Computer Science
Image collages are a popular tool for visualizing a collection of images, allowing users to display multiple images in a single composition. However, most existing methods for generating image collages are limited to simple shapes, such as rectangles or circles, which restrict their use in artistic and creative settings. Additionally, methods that can generate irregularly-shaped image collages often result in image overlapping and excessive blank space, rendering them ineffective for information communication.
In this paper, the authors introduce a novel algorithm called Shape-Aware Slicing that addresses the challenge of creating image collages of arbitrary shapes in an informative and visually pleasing manner. The algorithm partitions the input shape into cells using the medial axis and binary slicing tree. This approach takes into account human perception and shape structure to generate visually pleasing partitions.
Furthermore, the authors optimize the layout of the collage by analyzing the input images to maximize the total salient regions. By doing so, they ensure that important features in the images are prominently displayed in the collage. The proposed algorithm is then evaluated through extensive experiments, comparing the results against previous work and existing commercial tools.
The evaluations demonstrate that the proposed algorithm efficiently arranges image collections on irregular shapes and generates visually superior results compared to previous work and existing commercial tools. This advancement opens up new possibilities for artists and designers who want to create image collages that break free from traditional rectangular or circular layouts.
By allowing for arbitrary shapes and optimizing the arrangement based on salient regions, this algorithm enables users to create visually compelling image collages that effectively communicate information. Future research could explore further optimizations or extensions of the algorithm, such as incorporating user preferences or incorporating machine learning techniques to automatically select the most salient regions.
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by jsendak | Jan 8, 2024 | Computer Science
Expert Commentary: Monotonic Relationship between Coherence of Illumination and Computer Vision Performance
The recent study presented in this article sheds light on the relationship between the degree of coherence of illumination and performance in various computer vision tasks. By simulating partially coherent illumination using computational methods, researchers were able to investigate the impact of coherent length on image entropy, object recognition, and depth sensing performance.
Understanding Coherence of Illumination
Coherence of illumination refers to the degree to which the phase relationships between different points in a lightwave are maintained. An ideal coherent lightwave has perfect phase relationships, while partially coherent lightwave exhibits some random phase variations. In computer vision, coherence of illumination plays a crucial role in determining the quality of images and the accuracy of different vision tasks.
Effect on Image Entropy
One of the interesting findings of this study is the positive correlation between increasing coherent length and improved image entropy. Image entropy represents the amount of randomness or information content in an image. Higher entropy indicates more varied and detailed features, leading to better visual representation. The researchers’ use of computational methods to mimic partially coherent illumination enabled them to observe how coherence affects image entropy.
Enhanced Object Recognition
The impact of coherence on object recognition performance is another important aspect highlighted in this study. By employing a deep neural network for object recognition tasks, the researchers found that increased coherent length led to better object recognition results. This suggests that more coherent illumination provides clearer and more distinctive visual cues, improving the model’s ability to classify and identify objects accurately.
Improved Depth Sensing Performance
In addition to object recognition, the researchers also explored the relationship between coherence of illumination and depth sensing performance. Depth sensing is crucial in applications like robotics, augmented reality, and autonomous driving. The study revealed a positive correlation between increased coherent length and enhanced depth sensing accuracy. This indicates that more coherent illumination allows for better depth estimation and reconstruction, enabling more precise understanding of a scene’s 3D structure.
Future Implications
The results of this study provide valuable insights into the importance of coherence of illumination in computer vision tasks. By further refining and understanding the relationship between coherence and performance, researchers can potentially develop novel techniques to improve computer vision systems.
For instance, the findings could be leveraged to optimize lighting conditions in imaging systems, such as cameras and sensors used for object recognition or depth sensing. Additionally, advancements in computational methods for simulating partially coherent illumination could enable more accurate modeling and analysis of real-world scenarios.
Furthermore, these findings could also guide the development of new algorithms and models that take into account the coherence of illumination, leading to more robust computer vision systems capable of handling complex visual environments.
Overall, this study paves the way for future research in understanding the interplay between coherence of illumination and computer vision performance. It opens up avenues for further exploration and innovations in the field of computer vision, with the potential to drive advancements in diverse applications such as autonomous systems, medical imaging, and surveillance.
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by jsendak | Jan 8, 2024 | Computer Science
The current landscape of research leveraging large language models (LLMs) is
experiencing a surge. Many works harness the powerful reasoning capabilities of
these models to comprehend various modalities, such as text, speech, images,
videos, etc. They also utilize LLMs to understand human intention and generate
desired outputs like images, videos, and music. However, research that combines
both understanding and generation using LLMs is still limited and in its
nascent stage. To address this gap, we introduce a Multi-modal Music
Understanding and Generation (M$^{2}$UGen) framework that integrates LLM’s
abilities to comprehend and generate music for different modalities. The
M$^{2}$UGen framework is purpose-built to unlock creative potential from
diverse sources of inspiration, encompassing music, image, and video through
the use of pretrained MERT, ViT, and ViViT models, respectively. To enable
music generation, we explore the use of AudioLDM 2 and MusicGen. Bridging
multi-modal understanding and music generation is accomplished through the
integration of the LLaMA 2 model. Furthermore, we make use of the MU-LLaMA
model to generate extensive datasets that support text/image/video-to-music
generation, facilitating the training of our M$^{2}$UGen framework. We conduct
a thorough evaluation of our proposed framework. The experimental results
demonstrate that our model achieves or surpasses the performance of the current
state-of-the-art models.
The Multi-modal Music Understanding and Generation (M$^{2}$UGen) Framework: Advancing Research in Large Language Models
In recent years, research leveraging large language models (LLMs) has gained significant momentum. These models have demonstrated remarkable capabilities in understanding and generating various modalities such as text, speech, images, and videos. However, there is still a gap when it comes to combining understanding and generation using LLMs, especially in the context of music. The M$^{2}$UGen framework aims to bridge this gap by integrating LLMs’ abilities to comprehend and generate music across different modalities.
Multimedia information systems, animations, artificial reality, augmented reality, and virtual realities are all interconnected fields that rely on the integration of different modalities to create immersive and interactive experiences. The M$^{2}$UGen framework embodies the multi-disciplinary nature of these fields by leveraging pretrained models like MERT for text understanding, ViT for image understanding, and ViViT for video understanding. By combining these models, the framework enables creative potential to be unlocked from diverse sources of inspiration.
To facilitate music generation, the M$^{2}$UGen framework utilizes AudioLDM 2 and MusicGen. These components provide the necessary tools and techniques for generating music based on the understanding obtained from LLMs. However, what truly sets M$^{2}$UGen apart is its ability to bridge multi-modal understanding and music generation through the integration of the LLaMA 2 model. This integration allows for a seamless translation of comprehended multi-modal inputs into musical outputs.
Furthermore, the MU-LLaMA model plays a crucial role in supporting the training of the M$^{2}$UGen framework. By generating extensive datasets that facilitate text/image/video-to-music generation, MU-LLaMA enables the framework to learn and improve its music generation capabilities. This training process ensures that the M$^{2}$UGen framework achieves or surpasses the performance of the current state-of-the-art models.
In the wider field of multimedia information systems, the M$^{2}$UGen framework represents a significant advancement. Its ability to comprehend and generate music across different modalities opens up new possibilities for creating immersive multimedia experiences. By combining the power of LLMs with various pretrained models and techniques, the framework demonstrates the potential for pushing the boundaries of what is possible in animations, artificial reality, augmented reality, and virtual realities.
In conclusion, the M$^{2}$UGen framework serves as a pivotal contribution to research leveraging large language models. Its integration of multi-modal understanding and music generation showcases the synergistic potential of combining different modalities. As this field continues to evolve and mature, we can expect further advancements in the realm of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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