Viral News

S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
14 Aug

S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation

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arXiv:2408.06447v1 Announce Type: new Abstract: Medical image segmentation has been traditionally approached by training or fine-tuning the entire model to cater to any new modality or dataset. However, this approach often requires tuning a large number of parameters during training. With the introduction of the Segment Anything Model (SAM) for prompted segmentation of natural images, many efforts have been made towards adapting it efficiently for medical imaging, thus reducing the training time and resources. However, these methods still require expert annotations for every image in the form of point prompts or bounding box prompts during training and inference, making it…
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Social Debiasing for Fair Multi-modal LLMs
14 Aug

Social Debiasing for Fair Multi-modal LLMs

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arXiv:2408.06569v1 Announce Type: new Abstract: Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities. However, these models often inherit severe social biases from their training datasets, leading to unfair predictions based on attributes like race and gender. This paper addresses the issue of social biases in MLLMs by i) Introducing a comprehensive Counterfactual dataset with Multiple Social Concepts (CMSC), which provides a more diverse and extensive training set compared to existing datasets. ii) Proposing an Anti-Stereotype Debiasing strategy (ASD). Our method works by revisiting the MLLM training process, rescaling the autoregressive loss function, and improving data…
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Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
14 Aug

Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction

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

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

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

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

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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
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Fooling SHAP with Output Shuffling Attacks
14 Aug

Fooling SHAP with Output Shuffling Attacks

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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
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Wavelet based inpainting detection
14 Aug

Wavelet based inpainting detection

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

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

stp2y0 CommentsViral News
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
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How AI-Driven Network Monitoring is Revolutionizing AIOps
14 Aug

How AI-Driven Network Monitoring is Revolutionizing AIOps

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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.…
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