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13 Aug

How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model

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arXiv:2408.05411v1 Announce Type: new Abstract: Understanding and predicting viewer attention in omnidirectional videos (ODVs) is crucial for enhancing user engagement in virtual and augmented reality applications. Although both audio and visual modalities are essential for saliency prediction in ODVs, the joint exploitation of these two modalities has been limited, primarily due to the absence of large-scale audio-visual saliency databases and comprehensive analyses. This paper comprehensively investigates audio-visual attention in ODVs from both subjective and objective perspectives. Specifically, we first introduce a new audio-visual saliency database for omnidirectional videos, termed AVS-ODV database, containing 162 ODVs and corresponding eye movement data collected…

13 Aug

MABR: A Multilayer Adversarial Bias Removal Approach Without Prior Bias Knowledge

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arXiv:2408.05497v1 Announce Type: new Abstract: Models trained on real-world data often mirror and exacerbate existing social biases. Traditional methods for mitigating these biases typically require prior knowledge of the specific biases to be addressed, such as gender or racial biases, and the social groups associated with each instance. In this paper, we introduce a novel adversarial training strategy that operates independently of prior bias-type knowledge and protected attribute labels. Our approach proactively identifies biases during model training by utilizing auxiliary models, which are trained concurrently by predicting the performance of the main model without relying on task labels. Additionally, we…

13 Aug

EclipseNETs: a differentiable description of irregular eclipse conditions

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[Submitted on 9 Aug 2024] View a PDF of the paper titled EclipseNETs: a differentiable description of irregular eclipse conditions, by Giacomo Acciarini and 2 other authors View PDF HTML (experimental) Abstract:In the field of spaceflight mechanics and astrodynamics, determining eclipse regions is a frequent and critical challenge. This determination impacts various factors, including the acceleration induced by solar radiation pressure, the spacecraft power input, and its thermal state all of which must be accounted for in various phases of the mission design. This study leverages recent advances in neural image processing to develop fully differentiable models of eclipse regions…

13 Aug

RSL-BA: Rolling Shutter Line Bundle Adjustment

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arXiv:2408.05409v1 Announce Type: new Abstract: The line is a prevalent element in man-made environments, inherently encoding spatial structural information, thus making it a more robust choice for feature representation in practical applications. Despite its apparent advantages, previous rolling shutter bundle adjustment (RSBA) methods have only supported sparse feature points, which lack robustness, particularly in degenerate environments. In this paper, we introduce the first rolling shutter line-based bundle adjustment solution, RSL-BA. Specifically, we initially establish the rolling shutter camera line projection theory utilizing Pl"ucker line parameterization. Subsequently, we derive a series of reprojection error formulations which are stable and efficient. Finally,…

13 Aug

Investigating Instruction Tuning Large Language Models on Graphs

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arXiv:2408.05457v1 Announce Type: new Abstract: Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks. This study delves into the capabilities of instruction-following LLMs for engaging with real-world graphs, aiming to offer empirical insights into how LLMs can effectively interact with graphs and generalize across graph tasks. We begin by constructing a dataset designed for instruction tuning, which comprises a diverse collection of 79 graph-related tasks from academic and e-commerce domains, featuring 44,240 training instances and 18,960 test samples. Utilizing this benchmark, our initial investigation focuses on identifying…

13 Aug

Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification

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arXiv:2408.05347v1 Announce Type: new Abstract: Unsupervised anomaly detection is a promising technique for identifying unusual patterns in data without the need for labeled training examples. This approach is particularly valuable for early case detection in epidemic management, especially when early-stage data are scarce. This research introduces a novel hybrid method for anomaly detection that combines distance and density measures, enhancing its applicability across various infectious diseases. Our method is especially relevant in pandemic situations, as demonstrated during the COVID-19 crisis, where traditional supervised classification methods fall short due to limited data. The efficacy of our method is evaluated using COVID-19…

13 Aug

Mesh deformation-based single-view 3D reconstruction of thin eyeglasses frames with differentiable rendering

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13 Aug

Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation

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arXiv:2408.05456v1 Announce Type: new Abstract: Unified graph representation learning aims to produce node embeddings, which can be applied to multiple downstream applications. However, existing studies based on graph neural networks and language models either suffer from the limitations of numerous training needed toward specific downstream predictions or have shallow semantic features. In this work, we propose a novel Path-LLM model to learn unified graph representation, which leverages a powerful large language model (LLM) to incorporate our proposed path features. Our Path-LLM framework consists of several well-designed techniques. First, we develop a new mechanism of long-to-short shortest path (L2SP) selection, which…

13 Aug

Benefits of Distributed Tracing in Improving Application Performance

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Your application starts lagging, and users are disheartened. They're leaving faster than you can figure out what's wrong. Is it a database query? A slow API call? Or maybe a service is overloaded? When every millisecond counts, these performance issues can seriously hurt user experience and impact your bottom line. These problems are becoming more frequent. More than 40% of companies are losing revenue because of downtime, cloud complexity, and outdated systems. So why is this happening? As applications grow, they depend on a network of connected services. Each service plays a vital role in providing a smooth user experience. But…

13 Aug

rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA

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[Submitted on 9 Aug 2024] View a PDF of the paper titled rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA, by Mohammad Mehdi Rahimifar and 2 other authors View PDF HTML (experimental) Abstract:Implementing Machine Learning (ML) models on Field-Programmable Gate Arrays (FPGAs) is becoming increasingly popular across various domains as a low-latency and low-power solution that helps manage large data rates generated by continuously improving detectors. However, developing ML models for FPGAs is time-consuming, as optimization requires synthesis to evaluate FPGA area and latency, making the process slow and repetitive. This paper introduces…