by jsendak | Nov 6, 2024 | Computer Science
arXiv:2411.02851v1 Announce Type: new
Abstract: The goal of Multilingual Visual Answer Localization (MVAL) is to locate a video segment that answers a given multilingual question. Existing methods either focus solely on visual modality or integrate visual and subtitle modalities. However, these methods neglect the audio modality in videos, consequently leading to incomplete input information and poor performance in the MVAL task. In this paper, we propose a unified Audio-Visual-Textual Span Localization (AVTSL) method that incorporates audio modality to augment both visual and textual representations for the MVAL task. Specifically, we integrate features from three modalities and develop three predictors, each tailored to the unique contributions of the fused modalities: an audio-visual predictor, a visual predictor, and a textual predictor. Each predictor generates predictions based on its respective modality. To maintain consistency across the predicted results, we introduce an Audio-Visual-Textual Consistency module. This module utilizes a Dynamic Triangular Loss (DTL) function, allowing each modality’s predictor to dynamically learn from the others. This collaborative learning ensures that the model generates consistent and comprehensive answers. Extensive experiments show that our proposed method outperforms several state-of-the-art (SOTA) methods, which demonstrates the effectiveness of the audio modality.
Expert Commentary: Incorporating Audio Modality for Multilingual Visual Answer Localization
The Multilingual Visual Answer Localization (MVAL) task aims to identify a specific segment of a video that answers a given multilingual question. While previous methods have primarily focused on visual and textual modalities, the role of audio modality in videos has often been neglected. This paper introduces the Audio-Visual-Textual Span Localization (AVTSL) method, which integrates audio modality alongside visual and textual representations to enhance the performance of the MVAL task.
The AVTSL method takes advantage of the multi-disciplinary nature of multimedia information systems, specifically in the context of animations, artificial reality, augmented reality, and virtual realities. By incorporating features from three modalities, the proposed method provides a comprehensive understanding of the video content and improves the accuracy of the localization task.
One of the key contributions of this paper is the development of three predictors, each tailored to a specific modality. The audio-visual predictor utilizes both visual and audio features, the visual predictor focuses solely on visual features, and the textual predictor leverages textual representations. This multi-modal approach allows each predictor to capture the unique contributions of their respective modalities, resulting in more accurate predictions.
To ensure consistency across the predicted results, the AVTSL method introduces an Audio-Visual-Textual Consistency module. This module incorporates a Dynamic Triangular Loss (DTL) function, enabling collaborative learning between the predictors. By dynamically learning from each other, the predictors generate consistent and comprehensive answers. This is particularly important in the MVAL task, where the integration of multiple modalities is essential for accurate localization.
Extensive experiments have been conducted to evaluate the performance of the proposed AVTSL method. The results demonstrate that the inclusion of the audio modality significantly improves the performance compared to several state-of-the-art methods. This highlights the importance of considering audio information in addition to visual and textual data for the MVAL task.
In conclusion, the AVTSL method presented in this paper showcases the potential of incorporating audio modality for enhancing the accuracy of the Multilingual Visual Answer Localization task. By leveraging features from multiple modalities and employing collaborative learning, this method provides more comprehensive and consistent answers. The multi-disciplinary nature of this approach aligns with the wider field of multimedia information systems and its applications in animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Nov 6, 2024 | Computer Science
Fact-checking has become increasingly important in today’s digital age, as misinformation and fake news continue to spread rampantly. To combat this problem, fact-checking pipelines have adopted the Decompose-Then-Verify paradigm, breaking down texts into smaller claims for individual verification and then combining them for a final veracity decision. While decomposition is commonly used in these pipelines, its impact on the overall fact-checking performance is not well understood.
Past studies on the subject have yielded conflicting results. Some have reported improvements in fact-checking performance with decomposition, while others have observed declines. This indicates that the effects of decomposition are inconsistent and require further investigation. In an effort to fill this gap, the authors of this article present an in-depth analysis of the impact of decomposition on downstream verification performance.
The authors conducted error case inspections and experiments to examine the impact of decomposition more closely. Through this analysis, they introduced a categorization of decomposition errors and discovered a trade-off between accuracy gains and the noise introduced through decomposition. This trade-off highlights the challenge of balancing the benefits of decomposition with the potential drawbacks.
By providing a comprehensive analysis of decomposition errors and their impact on fact-checking performance, this study offers valuable insights into the current system’s instability. It also serves as a guide for future studies aimed at improving claim decomposition in fact-checking pipelines.
In conclusion, this article sheds light on an important aspect of fact-checking pipelines and highlights the need for further research in this area. Understanding the impact of decomposition on fact-checking performance is crucial for developing more effective and reliable fact-checking systems. By addressing the trade-offs and challenges associated with decomposition, we can improve the accuracy and efficiency of fact-checking processes, ultimately aiding in the fight against misinformation.
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by jsendak | Nov 5, 2024 | Computer Science
arXiv:2411.00813v1 Announce Type: new
Abstract: Personality analysis from online short videos has gained prominence due to its applications in personalized recommendation systems, sentiment analysis, and human-computer interaction. Traditional assessment methods, such as questionnaires based on the Big Five Personality Framework, are limited by self-report biases and are impractical for large-scale or real-time analysis. Leveraging the rich, multi-modal data present in short videos offers a promising alternative for more accurate personality inference. However, integrating these diverse and asynchronous modalities poses significant challenges, particularly in aligning time-varying data and ensuring models generalize well to new domains with limited labeled data. In this paper, we propose a novel multi-modal personality analysis framework that addresses these challenges by synchronizing and integrating features from multiple modalities and enhancing model generalization through domain adaptation. We introduce a timestamp-based modality alignment mechanism that synchronizes data based on spoken word timestamps, ensuring accurate correspondence across modalities and facilitating effective feature integration. To capture temporal dependencies and inter-modal interactions, we employ Bidirectional Long Short-Term Memory networks and self-attention mechanisms, allowing the model to focus on the most informative features for personality prediction. Furthermore, we develop a gradient-based domain adaptation method that transfers knowledge from multiple source domains to improve performance in target domains with scarce labeled data. Extensive experiments on real-world datasets demonstrate that our framework significantly outperforms existing methods in personality prediction tasks, highlighting its effectiveness in capturing complex behavioral cues and robustness in adapting to new domains.
Personality Analysis from Online Short Videos
Personality analysis from online short videos is an emerging field that has gained prominence due to its applications in personalized recommendation systems, sentiment analysis, and human-computer interaction. This article presents a novel multi-modal personality analysis framework that addresses the challenges of integrating diverse and asynchronous modalities, aligning time-varying data, and ensuring model generalization to new domains with limited labeled data.
Traditionally, personality assessment methods rely on self-report questionnaires based on the Big Five Personality Framework. However, these methods are limited by self-report biases and are impractical for large-scale or real-time analysis. Leveraging rich, multi-modal data from short videos offers a promising alternative for more accurate personality inference.
The multi-disciplinary nature of this framework is evident in its incorporation of concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By utilizing multiple modalities such as visual information, audio cues, and spoken words, the framework is able to capture complex behavioral cues and enhance personality prediction.
To overcome the challenge of aligning time-varying data from different modalities, the framework introduces a timestamp-based modality alignment mechanism. This mechanism synchronizes the data based on spoken word timestamps, ensuring accurate correspondence across modalities and facilitating effective feature integration.
To capture temporal dependencies and inter-modal interactions, the framework utilizes Bidirectional Long Short-Term Memory networks and self-attention mechanisms. These techniques allow the model to focus on the most informative features for personality prediction, taking into account both past and future context.
Furthermore, the framework introduces a gradient-based domain adaptation method to improve performance in target domains with limited labeled data. By transferring knowledge from multiple source domains, the model gains the ability to adapt to new domains and generalize well.
The article highlights the effectiveness of the proposed framework through extensive experiments on real-world datasets. The framework significantly outperforms existing methods in personality prediction tasks, demonstrating its ability to capture complex behavioral cues and its robustness in adapting to new domains.
Conclusion
Personality analysis from online short videos is a fascinating and challenging research area with broad applications. The multi-modal personality analysis framework presented in this article provides a comprehensive solution to the challenges of integrating diverse data modalities, aligning time-varying data, and ensuring model generalization. Its incorporation of concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities demonstrates the multi-disciplinary nature of the field.
Future research in this area could focus on further improving the alignment mechanisms to handle more complex modalities, such as facial expressions and body language. Additionally, exploring the combination of virtual realities and augmented reality technologies could provide opportunities for more immersive and interactive personality analysis.
Reference: “Personality Analysis from Online Short Videos with Multi-Modal Fusion and Domain Adaptation” by [Authors], [Year].
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by jsendak | Nov 5, 2024 | Computer Science
Proactive Detection and Calibration of Seasonal Advertisements: Enhancing Ads Delivery Systems
In the ever-evolving world of digital advertising, numerous factors come into play to ensure optimal delivery and performance of ads. Among these factors, proactive detection and calibration of seasonal advertisements have emerged as key components that can significantly impact user experience and revenue. In this paper, we introduce Proactive Detection and Calibration of Seasonal Advertisements (PDCaSA) as a research problem that has captured the attention of the ads ranking and recommendation community, both in industry and academia.
The motivation behind PDCaSA lies in the need to effectively identify and adapt to seasonal trends in the advertising landscape. Seasonal advertisements, such as those related to holidays or specific events, often experience fluctuations in user engagement and conversion rates. By proactively detecting and calibrating these seasonal ads, advertisers can tailor their strategies and maximize their impact on users.
This paper offers detailed guidelines and insights into tackling the PDCaSA problem. The guidelines are derived from extensive testing and experimentation conducted in a large-scale industrial ads ranking system. The authors share their findings, which include a clear definition of the problem, its motivation based on real-world systems, and evaluation metrics to measure success. Furthermore, the paper sheds light on the existing challenges associated with data annotation and machine learning modeling techniques required to address this problem effectively.
One notable contribution of this research is the proposed solution for detecting seasonality in ads using Multimodal Language Models (MLMs). The authors demonstrate that by leveraging MLMs, they achieved an impressive top F1 score of 0.97 on an in-house benchmark. The use of MLMs is not limited to detecting seasonality alone; they also serve as valuable resources for knowledge distillation, machine labeling, and enhancing the ensembled and tiered seasonality detection system.
Based on the findings presented in this paper, it is evident that incorporating MLMs into ads ranking systems can provide enriched seasonal information, thereby improving the overall ad delivery process. Empowered with this knowledge, advertisers can make informed decisions and optimize their campaigns to align with seasonal trends and user preferences.
Looking Ahead
The introduction of PDCaSA as a research problem opens up several avenues for future exploration. Firstly, further investigation into the scalability and applicability of MLMs in large-scale ads ranking systems is warranted. While the authors have showcased promising results, it is essential to validate and fine-tune this approach in diverse advertising contexts.
Additionally, the paper highlights the challenges and best practices associated with data annotation and machine learning modeling, focusing on seasonality detection. Expanding on this aspect, future research could explore innovative techniques for enhancing data annotation efficiency and model interpretability, making the process more streamlined and accessible for ads ranking systems.
Another area ripe for exploration is the integration of multimodal information beyond language in ads ranking systems. By incorporating visual, audio, and contextual cues in addition to text-based MLMs, it may be possible to unlock deeper insights into ad performance and seasonal trends, leading to more holistic and effective ad delivery.
In conclusion, the research presented in this paper lays a solid foundation for addressing the proactive detection and calibration of seasonal advertisements. By leveraging multifaceted approaches such as MLMs, advertisers can stay ahead of the curve and optimize their campaigns based on seasonal dynamics. The insights and guidelines provided pave the way for further advancements in the field, positioning PDCaSA as a critical research problem in the ads ranking and recommendation community.
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by jsendak | Nov 4, 2024 | Computer Science
arXiv:2411.00304v1 Announce Type: cross
Abstract: In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle various complex tasks, yet issues such as hallucinations and weak object discrimination persist. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation. This paper addresses these challenges by proposing a unified approach that integrates the strengths of both paradigms. Considering interleaved image-text sequences as the general format of input samples, we introduce a structure-induced training strategy that imposes semantic relationships between input samples and the MLLM’s hidden state. This approach enhances the MLLM’s ability to capture global semantics and distinguish fine-grained semantics. By leveraging dynamic sequence alignment within the Dynamic Time Warping framework and integrating a novel kernel for fine-grained semantic differentiation, our method effectively balances generative and discriminative tasks. Extensive experiments demonstrate the effectiveness of our approach, achieving state-of-the-art results in multiple generative tasks, especially those requiring cognitive and discrimination abilities. Additionally, our method surpasses discriminative benchmarks in interleaved and fine-grained retrieval tasks. By employing a retrieval-augmented generation strategy, our approach further enhances performance in some generative tasks within one model, offering a promising direction for future research in vision-language modeling.
Integration of Generative and Discriminative Approaches in Vision-Language Models
Over the past few years, Vision-Language Models (VLMs) have made significant progress in understanding and generating text based on visual input. However, two predominant paradigms have emerged in training these models, each with its own limitations. Generative training has allowed Multimodal Large Language Models (MLLMs) to tackle various complex tasks, but issues like hallucinations and weak object discrimination still persist. On the other hand, discriminative training, exemplified by models like CLIP, performs well in zero-shot image-text classification and retrieval but struggles with more complex scenarios that require fine-grained semantic differentiation.
This paper proposes a unified approach that integrates the strengths of both paradigms to tackle these challenges. The authors consider interleaved image-text sequences as the general format of input samples and introduce a structure-induced training strategy that imposes semantic relationships between these input samples and the MLLM’s hidden state. By doing so, they enhance the model’s ability to capture global semantics and distinguish fine-grained semantics.
One interesting aspect of this approach is the use of dynamic sequence alignment within the Dynamic Time Warping framework. This helps align the image and text sequences, allowing for better understanding of the relationships between them. Additionally, the authors propose a novel kernel for fine-grained semantic differentiation, further enhancing the model’s discriminative abilities.
The multi-disciplinary nature of this work is evident in its connections to various fields. In the wider field of multimedia information systems, this work contributes by providing a more effective way of combining visual and textual information. By addressing the limitations of generative and discriminative models, the proposed approach opens up new possibilities for applications in animations, artificial reality, augmented reality, and virtual realities.
For example, in animations, this approach could improve the generation of text captions or dialogue based on visual scenes. It could also enhance the understanding of complex scenarios in virtual reality environments, allowing for more immersive experiences. Furthermore, in augmented reality applications, the integration of generative and discriminative approaches could enable better object recognition and understanding of the surrounding environment.
The experiments conducted by the authors demonstrate the effectiveness of their approach, achieving state-of-the-art results in multiple generative tasks, particularly those requiring cognitive and discrimination abilities. Additionally, their method surpasses discriminative benchmarks in interleaved and fine-grained retrieval tasks.
By employing a retrieval-augmented generation strategy, the authors further enhance the performance of generative tasks within one model, offering a promising direction for future research in vision-language modeling. This integration of retrieval and generation could lead to breakthroughs in areas such as interactive storytelling, where the model can generate text based on retrieved information from a large knowledge base.
In conclusion, the unified approach proposed in this paper addresses the challenges of generative and discriminative training in Vision-Language Models by integrating the strengths of both paradigms. The multi-disciplinary nature of this work allows it to have implications in the broader field of multimedia information systems and its related domains, such as animations, artificial reality, augmented reality, and virtual realities. The experiments presented demonstrate the effectiveness of the proposed approach, and the retrieval-augmented generation strategy opens up exciting possibilities for future research in vision-language modeling.
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by jsendak | Nov 4, 2024 | Computer Science
Analyzing an Innovative Learning Framework for Inferring Governing Equations from Stochastic Dynamics
In the world of scientific research, understanding and predicting the behavior of stochastic systems is an ongoing challenge. Stochastic dynamics, which involve random fluctuations, can be found in many fields such as physics, biology, and finance. The ability to accurately infer the governing equations that describe the underlying dynamics is of great importance in these domains.
In a recent study, researchers have developed a novel learning framework that addresses this challenge. The framework combines trajectory data, generated by stochastic dynamics, with a noise structure to infer the governing equations. The researchers’ approach stands out for its ability to efficiently capture both the noise and the drift terms.
One notable feature of this learning framework is its ability to accommodate a wide range of noise types. In real-world scenarios, the noise affecting the system can exhibit various types of behavior, including being correlated and dependent on the system’s current state. By incorporating this flexibility, the method demonstrates its potential in capturing the complexity of stochastic systems.
Scalability is also a key advantage of this innovative learning framework. High-dimensional systems are commonplace in many fields, and being able to handle them is crucial for practical applications. The researchers have shown that their method performs well even in these complex scenarios, paving the way for the framework’s potential in real-world problems.
The exceptional performance of this learning algorithm in reconstructing the underlying dynamics has been extensively demonstrated through numerical experiments. By accurately inferring the governing equations, scientists can gain deeper insights into the behavior of stochastic systems. This, in turn, opens up new possibilities for prediction, control, and optimization.
The implications of this research are far-reaching. In the field of biology, for example, understanding the dynamics of gene regulatory networks, which are inherently stochastic, can provide valuable insights into cellular processes and diseases. In finance, predicting the behavior of stock prices or predicting market trends requires accurate models of stochastic dynamics.
By incorporating the noise structure and efficiently capturing both the noise and drift terms, this learning framework has the potential to revolutionize our ability to infer governing equations from stochastic dynamics in various fields.
Moving forward, future research could focus on exploring the limitations of the learning framework. While it has shown promising results in reconstructing the underlying stochastic dynamics, there may be scenarios in which the method faces challenges or fails to capture certain types of behavior accurately. Investigating these limitations can lead to further improvements and refinements of the framework, making it even more powerful and versatile.
The development of this innovative learning framework marks a significant step towards unraveling the complexities of stochastic dynamics. As scientists continue to explore and refine these methods, our understanding of the behavior of stochastic systems will continue to improve, leading to new applications and advancements across a range of fields.
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