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Style-Preserving Lip Sync via Audio-Aware Style Reference
13 Aug

Style-Preserving Lip Sync via Audio-Aware Style Reference

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arXiv:2408.05412v1 Announce Type: new Abstract: Audio-driven lip sync has recently drawn significant attention due to its widespread application in the multimedia domain. Individuals exhibit distinct lip shapes when speaking the same utterance, attributed to the unique speaking styles of individuals, posing a notable challenge for audio-driven lip sync. Earlier methods for such task often bypassed the modeling of personalized speaking styles, resulting in sub-optimal lip sync conforming to the general styles. Recent lip sync techniques attempt to guide the lip sync for arbitrary audio by aggregating information from a style reference video, yet they can not preserve the speaking styles…
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Your Context Is Not an Array: Unveiling Random Access Limitations in Transformers
13 Aug

Your Context Is Not an Array: Unveiling Random Access Limitations in Transformers

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[Submitted on 10 Aug 2024] View a PDF of the paper titled Your Context Is Not an Array: Unveiling Random Access Limitations in Transformers, by MohammadReza Ebrahimi and 2 other authors View PDF HTML (experimental) Abstract:Despite their recent successes, Transformer-based large language models show surprising failure modes. A well-known example of such failure modes is their inability to length-generalize: solving problem instances at inference time that are longer than those seen during training. In this work, we further explore the root cause of this failure by performing a detailed analysis of model behaviors on the simple parity task. Our analysis…
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SAMSA: Efficient Transformer for Many Data Modalities
13 Aug

SAMSA: Efficient Transformer for Many Data Modalities

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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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How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model
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…
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MABR: A Multilayer Adversarial Bias Removal Approach Without Prior Bias Knowledge
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…
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EclipseNETs: a differentiable description of irregular eclipse conditions
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…
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RSL-BA: Rolling Shutter Line Bundle Adjustment
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,…
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Investigating Instruction Tuning Large Language Models on Graphs
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…
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Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification
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…
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Mesh deformation-based single-view 3D reconstruction of thin eyeglasses frames with differentiable rendering
13 Aug

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

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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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