Viral News

HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization

HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization

arXiv:2408.06437v1 Announce Type: new Abstract: Online video understanding often relies on individual frames, leading to frame-by-frame predictions. Recent advancements such as Online Temporal Action Localization (OnTAL), extend this approach to instance-level predictions. However, existing methods mainly focus on short-term context, neglecting historical information. To address this, we introduce the History-Augmented Anchor Transformer (HAT) Framework for OnTAL. By integrating historical context, our framework enhances the synergy between long-term and short-term information, improving the quality of anchor features crucial for classification and localization. We evaluate our model on both procedural egocentric (PREGO) datasets (EGTEA and EPIC) and standard non-PREGO OnTAL datasets (THUMOS…
Read More
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies

AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
Read More
Fooling SHAP with Output Shuffling Attacks

Fooling SHAP with Output Shuffling Attacks

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
Read More
Wavelet based inpainting detection

Wavelet based inpainting detection

arXiv:2408.06429v1 Announce Type: new Abstract: With the advancement in image editing tools, manipulating digital images has become alarmingly easy. Inpainting, which is used to remove objects or fill in parts of an image, serves as a powerful tool for both image restoration and forgery. This paper introduces a novel approach for detecting image inpainting forgeries by combining DT-CWT with Hierarchical Feature segmentation and with noise inconsistency analysis. The DT-CWT offers several advantages for this task, including inherent shift-invariance, which makes it robust to minor manipulations during the inpainting process, and directional selectivity, which helps capture subtle artifacts introduced by inpainting…
Read More
Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data

Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
Read More
How AI-Driven Network Monitoring is Revolutionizing AIOps

How AI-Driven Network Monitoring is Revolutionizing AIOps

Introduction  Maintaining your computer network performance is vital for smooth business operations in today's fast-changing digital world. Regular network and performance monitoring of software is important, but it often does not give enough details or early warnings to handle complicated IT setups.  Moreover, there are instances where the monitoring software is incompetent to handle the data on a daily basis. This is where Artificial Intelligence for IT Operations (AIOps) comes in. It is changing the way we manage networks and performance metrics.  Brief overview of traditional network monitoring challenges  Traditional network monitoring solutions depend on fixed rules to spot problems.…
Read More
Implicit Neural Representation For Accurate CFD Flow Field Prediction

Implicit Neural Representation For Accurate CFD Flow Field Prediction

arXiv:2408.06486v1 Announce Type: new Abstract: Despite the plethora of deep learning frameworks for flow field prediction, most of them deal with flow fields on regular domains, and although the best ones can cope with irregular domains, they mostly rely on graph networks, so that real industrial applications remain currently elusive. We present a deep learning framework for 3D flow field prediction applied to blades of aircraft engine turbines and compressors. Crucially, we view any 3D field as a function from coordinates that is modeled by a neural network we call the backbone-net. It inherits the property of coordinate-based MLPs, namely…
Read More
Using deep learning to enhance electronic service quality: Application to real estate websites

Using deep learning to enhance electronic service quality: Application to real estate websites

arXiv:2408.06364v1 Announce Type: new Abstract: Electronic service quality (E-SQ) is a strategic metric for successful e-services.Among the service quality dimensions, tangibility is overlooked. However, by incorporating visuals or tangible tools, the intangible nature of e-services can be balanced. Thanks to advancements in Deep Learning for computer vision, tangible visual features can now be leveraged to enhance the browsing and searching experience of electronic services. Users usually have specific search criteria to meet, but most services will not offer flexible search filters. This research emphasizes the importance of integrating visual and descriptive features to improve the tangibility and efficiency of e-services.…
Read More
Chain-of-Strategy Planning with LLMs: Aligning the Generation of Psychotherapy Dialogue with Strategy in Motivational Interviewing

Chain-of-Strategy Planning with LLMs: Aligning the Generation of Psychotherapy Dialogue with Strategy in Motivational Interviewing

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
Read More
Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach

Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
Read More
No widgets found. Go to Widget page and add the widget in Offcanvas Sidebar Widget Area.