by jsendak | Apr 4, 2024 | Computer Science
arXiv:2404.02731v1 Announce Type: cross
Abstract: Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the partial loss of pixel values, posing new challenges for RAW domain processes like demosaicing. The challenge intensifies as most research in the RAW domain is based on the premise that each pixel contains a value, making the straightforward adaptation of these methods to event camera demosaicing problematic. To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing. Our core motivation is to refine a general and widely applicable foundational model from the RGB domain for RAW domain processing, thereby broadening the model’s applicability within the entire imaging process. Our method harnesses multi-scale processing and space-to-depth techniques to ensure efficiency and reduce computing complexity. We also proposed the Pixel-focus Loss function for network fine-tuning to improve network convergence based on our discovery of a long-tailed distribution in training loss. Our method has undergone validation on the MIPI Demosaic Challenge dataset, with subsequent analytical experimentation confirming its efficacy. All code and trained models are released here: https://github.com/yunfanLu/ev-demosaic
Improving RAW Domain Processing for Event Cameras: A Swin-Transformer-based approach
In recent years, there has been significant progress in high-quality imaging guided by event cameras. Event cameras, also known as asynchronous or neuromorphic cameras, offer advantages over traditional cameras, such as high temporal resolution, low-latency, and high dynamic range imaging capabilities. However, the unique sensor design of event cameras introduces challenges in processing the raw data captured by these cameras, specifically in the RAW domain.
The RAW domain refers to the unprocessed pixel level data captured by a camera before any demosaicing or other image processing is applied. Event cameras, unlike traditional cameras, do not capture a full-frame image at a fixed rate. Instead, they capture individual pixel events asynchronously as they occur, resulting in a sparsely distributed dataset with missing pixel values.
In this article, the authors highlight the need for improved demosaicing methods specifically tailored to event cameras in the RAW domain. Demosaicing is the process of reconstructing a full-color image from the incomplete color information captured by a camera’s sensor. Traditional demosaicing algorithms are designed for cameras that capture full-frame images, and they assume each pixel contains a value. However, event cameras do not provide complete pixel data, making the direct adaptation of these methods problematic.
The authors propose a solution that leverages the Swin-Transformer architecture, a state-of-the-art model originally designed for computer vision tasks in the RGB domain. The Swin-Transformer architecture has shown remarkable efficiency and effectiveness in capturing long-range dependencies and modeling image context. By adapting this architecture to the event camera’s RAW domain, the authors aim to improve the overall processing pipeline and broaden the applicability of the model within the entire imaging process.
In addition to the Swin-Transformer backbone, the authors introduce a novel loss function called the Pixel-focus Loss. This loss function is designed to fine-tune the network and improve convergence during training. The authors discovered a long-tailed distribution in the training loss, indicating that certain pixel values require more attention and focus during the demosaicing process. The Pixel-focus Loss function addresses this issue and guides the network to prioritize these challenging pixels.
One key aspect of this research is its multidisciplinary nature. The authors combine concepts from computer vision, image processing, and artificial intelligence to tackle the unique challenges posed by event camera data in the RAW domain. By leveraging techniques such as multi-scale processing and space-to-depth transformations, the proposed method ensures efficiency and reduces computational complexity without sacrificing accuracy.
Overall, this research contributes to the field of multimedia information systems by addressing the specific challenges associated with event camera data in the RAW domain. The proposed approach combines deep learning models, like the Swin-Transformer, with tailored loss functions to improve demosaicing performance. The methods presented in this article have been validated on a benchmark dataset, demonstrating their efficacy and potential for further advancements in the field of event camera processing.
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by jsendak | Apr 4, 2024 | Computer Science
Exploring IPFS and Scuttlebutt as Alternatives to Internet Protocols
As our reliance on the Internet continues to grow, it becomes increasingly important to explore alternative approaches to the current state of Internet protocols. Two such alternatives, IPFS (Interplanetary File System) and Scuttlebutt, have gained attention in recent years. This article aims to analyze the political implications and debates surrounding these technical enterprises through the lens of critical discourse analysis.
Infrastructural criticism, a form of criticism directed towards Internet regimes, has been gaining momentum in the digital space. By comparing IPFS and Scuttlebutt, we can gain a deeper understanding of the various dimensions of agency and the act of hijacking and substitution in decentralized protocols.
The Interplanetary File System (IPFS)
IPFS is an innovative approach to file sharing and web content distribution. It aims to create a distributed file system that is resilient, secure, and efficient. One of the key features of IPFS is its use of content addressing, where files are identified by their content rather than their location. This allows for greater scalability and redundancy, as files can be cached and distributed across multiple nodes.
The agency dimension of IPFS lies in its potential to challenge centralized control over the Internet. By enabling users to access and share content without depending on specific servers or hosting platforms, IPFS introduces a new level of autonomy and resilience. This decentralized approach has the potential to disrupt existing power dynamics and foster a more inclusive and democratic Internet.
Scuttlebutt: A Decentralized Social Network
Unlike traditional social networks that rely on centralized servers, Scuttlebutt takes a different approach by creating a decentralized framework for social interactions. Users store and share social data directly on their devices, creating a peer-to-peer network. This allows for offline communication and enables users to control their own data.
One of the distinct features of Scuttlebutt is its focus on local communities and offline-first design. It prioritizes local interactions and gradually synchronizes data across the network. This approach challenges the dominant discourse of global connectivity and emphasizes the importance of localized social networks.
The agency dimension of Scuttlebutt lies in its potential to empower individuals and communities by giving them control over their own social interactions and data. By enabling offline communication and emphasizing local connections, Scuttlebutt offers an alternative to the centralized and algorithmically-driven social networks that dominate the current Internet landscape.
Comparative Study and Future Implications
By analyzing IPFS and Scuttlebutt as case studies, we can identify the common technical similarity between the two systems – their decentralized nature. However, their approaches to decentralization differ significantly, highlighting the diverse possibilities within alternative Internet protocols.
Looking ahead, it is crucial to continue exploring and researching alternative protocols and infrastructures for the Internet. These explorations can help us better understand the political dimensions and potential implications of such alternatives. By embracing decentralized approaches and challenging the status quo, we can pave the way for a more democratic and resilient Internet ecosystem.
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by jsendak | Apr 3, 2024 | Computer Science
arXiv:2404.01336v1 Announce Type: cross
Abstract: Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content. However, these benchmarks typically focus solely on news pertaining to a single semantic topic or originating from a single platform, thereby failing to capture the diversity of multi-domain news in real scenarios. In order to understand fake news across various domains, the external knowledge and fine-grained annotations are indispensable to provide precise evidence and uncover the diverse underlying strategies for fabrication, which are also ignored by existing benchmarks. To address this gap, we introduce a novel multi-domain knowledge-enhanced benchmark with fine-grained annotations, named textbf{FineFake}. FineFake encompasses 16,909 data samples spanning six semantic topics and eight platforms. Each news item is enriched with multi-modal content, potential social context, semi-manually verified common knowledge, and fine-grained annotations that surpass conventional binary labels. Furthermore, we formulate three challenging tasks based on FineFake and propose a knowledge-enhanced domain adaptation network. Extensive experiments are conducted on FineFake under various scenarios, providing accurate and reliable benchmarks for future endeavors. The entire FineFake project is publicly accessible as an open-source repository at url{https://github.com/Accuser907/FineFake}.
Introducing FineFake: A Multi-Domain Knowledge-Enhanced Benchmark for Fake News Detection
Fake news has become a pervasive issue in today’s information landscape. As the spread of misinformation continues to grow, it is crucial to develop effective methods for detecting and combating fabricated news content. Existing benchmarks for fake news detection have made significant progress in this area, but they often fall short in capturing the diversity of multi-domain news.
This is where FineFake comes in. FineFake is a groundbreaking multi-domain knowledge-enhanced benchmark that goes beyond existing benchmarks by encompassing a wide range of semantic topics and platforms. With 16,909 data samples spanning six semantic topics and eight platforms, FineFake provides a comprehensive view of fake news across various domains.
What sets FineFake apart is its inclusion of external knowledge and fine-grained annotations. These additional layers of information enable us to provide precise evidence and uncover the diverse underlying strategies employed in fabricating news. By going beyond conventional binary labels, FineFake offers a deeper understanding of the complexity of fake news.
Notably, FineFake enriches each news item with multi-modal content, potential social context, and semi-manually verified common knowledge. This multidisciplinary approach allows us to analyze the news from multiple perspectives, taking into account both textual and visual elements. By including these diverse elements, FineFake reflects the multi-dimensional nature of multimedia information systems.
The release of FineFake also comes with three challenging tasks formulated based on the benchmark. These tasks provide a roadmap for future research and development in the field of fake news detection. To tackle these tasks, the authors propose a knowledge-enhanced domain adaptation network, which leverages the external knowledge integrated into FineFake. This approach highlights the importance of incorporating knowledge from different domains to effectively detect fake news.
In order to ensure the reliability and accuracy of the benchmark, extensive experiments have been conducted on FineFake under various scenarios. The results demonstrate its effectiveness and the potential it holds for future endeavors in fake news detection.
As a multidisciplinary benchmark, FineFake is not only relevant to the field of fake news detection but also closely connected to other areas such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The inclusion of multi-modal content aligns with the multimedia nature of these fields, while the knowledge-enhanced approach relates to the advancement of artificial reality and augmented reality technologies.
In conclusion, FineFake represents a significant step forward in the fight against fake news. By capturing the multi-domain nature of fake news and providing fine-grained annotations, FineFake opens up new possibilities for understanding and combating misinformation in our increasingly complex information ecosystem. It serves as a valuable resource and benchmark for researchers and practitioners alike, paving the way for future advancements in the field.
Reference: https://arxiv.org/abs/2404.01336v1
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by jsendak | Apr 3, 2024 | Computer Science
Abstract: This article discusses the use of sparsity approaches in Transformer-based Language Models to address the challenges of scalability and efficiency in training and inference. Transformer-based models have shown outstanding performance in Natural Language Processing (NLP) tasks, but their high resource requirements limit their widespread applicability. By examining the impact of sparsity on network topology, the authors draw inspiration from biological neuronal networks and propose NeuroPrune, a model-agnostic sparsity approach. Despite not focusing solely on performance optimization, NeuroPrune demonstrates competitive or superior performance compared to baselines on various NLP tasks, including classification and generation. Additionally, NeuroPrune significantly reduces training time and exhibits improvements in inference time in many cases.
Introduction
Transformer-based Language Models have revolutionized NLP with their exceptional performance across diverse tasks. However, their resource-intensive nature poses significant challenges in terms of training and inference efficiency. To overcome this hurdle, the authors explore the application of sparsity techniques inspired by biological networks.
Sparsity and Network Topology
The authors highlight the importance of understanding the impact of sparsity on network topology. They propose mechanisms such as preferential attachment and redundant synapse pruning that mimic the behavior of biological neuronal networks. By incorporating these principles into sparsity approaches, they aim to enhance the efficiency and performance of Transformer-based Language Models.
NeuroPrune: A Model-Agnostic Sparsity Approach
NeuroPrune is introduced as a principled, model-agnostic sparsity approach that leverages the insights from biological networks. It aims to address the challenges of scalability and efficiency in Transformer-based Language Models. Despite not solely focusing on performance optimization, NeuroPrune demonstrates competitive results compared to the baseline models on both classification and generation tasks in NLP.
Key Findings
NeuroPrune offers several noteworthy advantages over traditional models:
- Reduced Training Time: NeuroPrune achieves up to 10 times faster training time for a given level of sparsity compared to baselines. This improvement in efficiency is crucial for large-scale NLP applications.
- Improved Inference Time: In many cases, NeuroPrune exhibits measurable improvements in inference time. This benefit is particularly significant in real-time applications and systems where low latency is crucial.
- Competitive Performance: Despite not solely optimizing for performance, NeuroPrune performs on par with or surpasses baselines on various NLP tasks, including natural language inference, summarization, and machine translation.
Conclusion
The exploration of sparsity approaches in Transformer-based Language Models through the lens of network topology has yielded promising results. NeuroPrune, a model-agnostic sparsity approach inspired by biological networks, demonstrates competitive performance, reduced training time, and improvements in inference time. These findings open new avenues for addressing the scalability and efficiency challenges in NLP tasks, paving the way for broader applicability of Transformer-based models.
“By exploiting mechanisms seen in biological networks, NeuroPrune presents an innovative approach to sparsity in Transformer-based models. Its efficiency gains in training and inference time, coupled with its competitive performance, make it a compelling solution for large-scale NLP applications.”
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by jsendak | Apr 2, 2024 | Computer Science
arXiv:2403.20194v1 Announce Type: new
Abstract: This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts a three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity. Each level focuses on a distinct capability, mirroring the cognitive progression from basic perception to logical reasoning and ultimately to advanced creativity. ConvBench comprises 577 meticulously curated multi-turn conversations encompassing 215 tasks reflective of real-world demands. Automatic evaluations quantify response performance at each turn and overall conversation level. Leveraging the capability hierarchy, ConvBench enables precise attribution of conversation mistakes to specific levels. Experimental results reveal a performance gap between multi-modal models, including GPT4-V, and human performance in multi-turn conversations. Additionally, weak fine-grained perception in multi-modal models contributes to reasoning and creation failures. ConvBench serves as a catalyst for further research aimed at enhancing visual dialogues.
ConvBench: A Multi-Turn Conversation Evaluation Benchmark for Large Vision-Language Models
In the field of multimedia information systems, the development of Large Vision-Language Models (LVLMs) has gained significant attention. These models are designed to understand and generate text while also incorporating visual information. ConvBench, a novel benchmark presented in this paper, focuses on evaluating the performance of LVLMs in multi-turn conversations.
Unlike existing benchmarks that assess the capabilities of models in single-turn dialogues, ConvBench takes a multi-level approach. It mimics the cognitive processes of humans by dividing the evaluation into three levels: perception, reasoning, and creativity. This multi-modal capability hierarchy allows for a more comprehensive assessment of LVLM performance.
ConvBench comprises 577 carefully curated multi-turn conversations, covering 215 real-world tasks. Each conversation is automatically evaluated at every turn, as well as at the overall conversation level. This precise evaluation enables researchers to attribute mistakes to specific levels, facilitating a deeper understanding of model performance.
The results of experiments conducted using ConvBench highlight a performance gap between multi-modal models, including GPT4-V, and human performance in multi-turn conversations. This suggests that there is still room for improvement in LVLMs, particularly in the area of weak fine-grained perception, which contributes to failures in reasoning and creativity.
The concepts presented in ConvBench have far-reaching implications in the wider field of multimedia information systems. By incorporating both visual and textual information, LVLMs have the potential to revolutionize various applications such as animations, artificial reality, augmented reality, and virtual reality. These technologies heavily rely on the seamless integration of visuals and language, and ConvBench provides a benchmark for evaluating and improving the performance of LVLMs in these domains.
Furthermore, the multi-disciplinary nature of ConvBench, with its combination of perception, reasoning, and creativity, highlights the complex cognitive processes involved in human conversation. By studying and enhancing these capabilities in LVLMs, researchers can advance the field of artificial intelligence and develop models that come closer to human-level performance in engaging and meaningful conversations.
Conclusion
ConvBench is a pioneering multi-turn conversation evaluation benchmark that provides deep insights into the performance of Large Vision-Language Models. With its multi-modal capability hierarchy and carefully curated conversations, ConvBench enables precise evaluation and attribution of errors. The results of ConvBench experiments reveal the existing performance gap and the need for improvement in multi-modal models. The concepts presented in ConvBench have significant implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual reality. By advancing LVLMs, researchers can pave the way for more engaging and meaningful interactions between humans and machines.
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by jsendak | Apr 2, 2024 | Computer Science
Language and vision are undoubtedly two essential components of human intelligence. While humans have traditionally been the only example of intelligent beings, recent developments in artificial intelligence have provided us with new opportunities to study the contributions of language and vision to learning about the world. Through the creation of sophisticated Vision-Language Models (VLMs), researchers have gained insights into the role of these modalities in understanding the visual world.
The study discussed in this article focused on examining the impact of language on learning tasks using VLMs. By systematically removing different components from the cognitive architecture of these models, the researchers aimed to identify the specific contributions of language and vision to the learning process. Notably, they found that even without visual input, a language model leveraging all components was able to recover a majority of the VLM’s performance.
This finding suggests that language plays a crucial role in accessing prior knowledge and reasoning, enabling learning from limited data. It highlights the power of language in facilitating the transfer of knowledge and abstract understanding without relying solely on visual input. This insight not only has implications for the development of AI systems but also provides a deeper understanding of how humans utilize language to make sense of the visual world.
Moreover, this research leads us to ponder the broader implications of the relationship between language and vision in intelligence. How does language influence our perception and interpretation of visual information? Can language shape our understanding of the world even in the absence of direct sensory experiences? These are vital questions that warrant further investigation.
Furthermore, the findings of this study have practical implications for the development of AI systems. By understanding the specific contributions of language and vision, researchers can optimize the performance and efficiency of VLMs. Leveraging language to access prior knowledge can potentially enhance the learning capabilities of AI models, even when visual input is limited.
In conclusion, the emergence of Vision-Language Models presents an exciting avenue for studying the interplay between language and vision in intelligence. By using ablation techniques to dissect the contributions of different components, researchers are gaining valuable insights into how language enables learning from limited visual data. This research not only advances our understanding of AI systems but also sheds light on the fundamental nature of human intelligence and the role of language in shaping our perception of the visual world.
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