by jsendak | May 20, 2025 | Computer Science
arXiv:2505.12051v1 Announce Type: new
Abstract: The rapid rise of video content on platforms such as TikTok and YouTube has transformed information dissemination, but it has also facilitated the spread of harmful content, particularly hate videos. Despite significant efforts to combat hate speech, detecting these videos remains challenging due to their often implicit nature. Current detection methods primarily rely on unimodal approaches, which inadequately capture the complementary features across different modalities. While multimodal techniques offer a broader perspective, many fail to effectively integrate temporal dynamics and modality-wise interactions essential for identifying nuanced hate content. In this paper, we present CMFusion, an enhanced multimodal hate video detection model utilizing a novel Channel-wise and Modality-wise Fusion Mechanism. CMFusion first extracts features from text, audio, and video modalities using pre-trained models and then incorporates a temporal cross-attention mechanism to capture dependencies between video and audio streams. The learned features are then processed by channel-wise and modality-wise fusion modules to obtain informative representations of videos. Our extensive experiments on a real-world dataset demonstrate that CMFusion significantly outperforms five widely used baselines in terms of accuracy, precision, recall, and F1 score. Comprehensive ablation studies and parameter analyses further validate our design choices, highlighting the model’s effectiveness in detecting hate videos. The source codes will be made publicly available at https://github.com/EvelynZ10/cmfusion.
Expert Commentary: The Rise of Multimodal Approaches in Hate Video Detection
The proliferation of video content on social media platforms has brought about both positive and negative consequences. While it has democratized information dissemination and fostered creativity, it has also facilitated the spread of harmful content, such as hate videos. These videos often contain implicit messages that can be challenging to detect using traditional methods.
Current hate video detection approaches predominantly rely on unimodal techniques, which may not fully capture the complexity of multimedia content. Multimodal methods, on the other hand, leverage information from multiple modalities, such as text, audio, and video, to provide a more comprehensive understanding of the content. However, integrating temporal dynamics and modality-wise interactions in these approaches remains a challenge.
The CMFusion model introduced in this paper takes a step towards addressing this issue by utilizing a Channel-wise and Modality-wise Fusion Mechanism. By extracting features from different modalities and incorporating a temporal cross-attention mechanism, CMFusion aims to capture the nuanced relationships between video and audio streams. The model then processes these features using fusion modules to generate informative representations of hate videos.
Notably, the effectiveness of CMFusion is demonstrated through extensive experiments on a real-world dataset, where it outperforms five popular baselines in terms of accuracy, precision, recall, and F1 score. Ablation studies and parameter analyses further validate the design choices of the model, emphasizing its robustness in hate video detection.
From a multidisciplinary perspective, the development of CMFusion touches upon various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. As hate videos can manifest in different forms across these modalities, a holistic approach that combines insights from diverse disciplines is essential in combating harmful content online.
In conclusion, the integration of multimodal techniques, like CMFusion, represents a promising direction in addressing the challenges of hate video detection. By leveraging the complementary features of different modalities and incorporating advanced fusion mechanisms, researchers can enhance the accuracy and effectiveness of automated content moderation systems in the digital age.
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by jsendak | May 20, 2025 | Computer Science
Expert Commentary: Enhancing Reinforcement Learning in Large Language Models
Reinforcement Learning (RL) has become a key technique in improving the reasoning abilities of large language models (LLMs) such as DeepSeek-R1. One popular RL method, Group Relative Policy Optimization (GRPO), has been successful in training these models, but faces challenges when all sampled responses in a group are incorrect, leading to what is known as an “all-negative-sample” group. This can hinder learning progress as GRPO fails to update the policy in such cases.
The recent paper introduces a novel framework to address this issue by introducing response diversity within these all-negative-sample groups using AI feedback. The addition of this diversification not only improves learning dynamics, as shown through theoretical analysis, but also leads to enhanced performance across different model sizes and learning settings in offline and online scenarios.
This research contributes significantly to the understanding of learning dynamics in RL for LLMs, building upon recent insights from related work. By showing the feasibility and benefits of learning from all-negative-sample groups, this work opens up new avenues for enhancing the performance and capabilities of language models through reinforcement learning techniques.
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by jsendak | May 19, 2025 | Computer Science
arXiv:2505.11237v1 Announce Type: new
Abstract: Metaphorical imagination, the ability to connect seemingly unrelated concepts, is fundamental to human cognition and communication. While understanding linguistic metaphors has advanced significantly, grasping multimodal metaphors, such as those found in internet memes, presents unique challenges due to their unconventional expressions and implied meanings. Existing methods for multimodal metaphor identification often struggle to bridge the gap between literal and figurative interpretations. Additionally, generative approaches that utilize large language models or text-to-image models, while promising, suffer from high computational costs. This paper introduces textbf{C}oncept textbf{D}rift textbf{G}uided textbf{L}ayerNorm textbf{T}uning (textbf{CDGLT}), a novel and training-efficient framework for multimodal metaphor identification. CDGLT incorporates two key innovations: (1) Concept Drift, a mechanism that leverages Spherical Linear Interpolation (SLERP) of cross-modal embeddings from a CLIP encoder to generate a new, divergent concept embedding. This drifted concept helps to alleviate the gap between literal features and the figurative task. (2) A prompt construction strategy, that adapts the method of feature extraction and fusion using pre-trained language models for the multimodal metaphor identification task. CDGLT achieves state-of-the-art performance on the MET-Meme benchmark while significantly reducing training costs compared to existing generative methods. Ablation studies demonstrate the effectiveness of both Concept Drift and our adapted LN Tuning approach. Our method represents a significant step towards efficient and accurate multimodal metaphor understanding. The code is available: href{https://github.com/Qianvenh/CDGLT}{https://github.com/Qianvenh/CDGLT}.
Expert Commentary
The ability to understand and convey metaphors is a crucial aspect of human communication and cognition. When it comes to multimodal metaphors, such as those seen in internet memes, the challenges are unique due to their unconventional expressions and implied meanings. This paper introduces the CDGLT framework, which aims to address these challenges in a training-efficient manner.
The CDGLT framework incorporates innovative concepts like Concept Drift, which leverages cross-modal embeddings to generate new, divergent concept embeddings. This helps bridge the gap between literal features and the figurative task of identifying multimodal metaphors. Additionally, the prompt construction strategy utilized in CDGLT adapts feature extraction and fusion methods using pre-trained language models, further enhancing the framework’s effectiveness.
From a multidisciplinary perspective, this research combines concepts from natural language processing, computer vision, and cognitive psychology to develop a solution for multimodal metaphor identification. By tapping into the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, the CDGLT framework showcases the potential for interdisciplinary collaboration in advancing understanding of complex cognitive processes.
Furthermore, the state-of-the-art performance of CDGLT on the MET-Meme benchmark highlights its efficacy in tackling the challenges posed by multimodal metaphors. The reduced training costs compared to existing generative methods make CDGLT a promising tool for researchers and practitioners interested in multimodal metaphor understanding.
In conclusion, the CDGLT framework represents a significant contribution to the field of multimodal metaphor identification, paving the way for more efficient and accurate methods of analyzing complex and layered forms of communication.
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by jsendak | May 19, 2025 | Computer Science
Expert Commentary: Analyzing Queuing System Distribution Functions
Queuing systems play a crucial role in various real-world applications, such as telecommunications, traffic management, and customer service. Understanding the distribution functions of busy periods and busy cycles in these systems is essential for optimizing their performance and resource utilization.
In the case of the M|G|$infty$ queue system, the lack of closed-form formulas for these distribution functions presents a significant challenge. However, the M|D|$infty$ queue stands out due to its Laplace transform expressions in closed form.
Platzman, Ammons, and Bartholdi III have developed an algorithm that leverages these closed-form expressions to compute tail probabilities efficiently. This algorithm opens up opportunities for precise calculations and analysis of distribution functions in the M|D|$infty$ queue system.
Implementation through FORTRAN Program
By implementing the algorithm in a FORTRAN program, researchers and practitioners can harness its computational power to explore complex queuing system scenarios. The program enables them to calculate tail probabilities accurately and derive valuable insights into system performance under different conditions.
Overall, the development and implementation of algorithms like the one proposed by Platzman, Ammons, and Bartholdi III are instrumental in advancing our understanding of queuing systems and enhancing their efficiency and reliability in practical applications.
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by jsendak | May 16, 2025 | Computer Science
arXiv:2505.09936v1 Announce Type: cross
Abstract: The rapid development of generative artificial intelligence (GenAI) presents new opportunities to advance the cartographic process. Previous studies have either overlooked the artistic aspects of maps or faced challenges in creating both accurate and informative maps. In this study, we propose CartoAgent, a novel multi-agent cartographic framework powered by multimodal large language models (MLLMs). This framework simulates three key stages in cartographic practice: preparation, map design, and evaluation. At each stage, different MLLMs act as agents with distinct roles to collaborate, discuss, and utilize tools for specific purposes. In particular, CartoAgent leverages MLLMs’ visual aesthetic capability and world knowledge to generate maps that are both visually appealing and informative. By separating style from geographic data, it can focus on designing stylesheets without modifying the vector-based data, thereby ensuring geographic accuracy. We applied CartoAgent to a specific task centered on map restyling-namely, map style transfer and evaluation. The effectiveness of this framework was validated through extensive experiments and a human evaluation study. CartoAgent can be extended to support a variety of cartographic design decisions and inform future integrations of GenAI in cartography.
Expert Commentary: The Future of Cartography with Generative AI
In the age of rapid technological advancements, the integration of generative artificial intelligence (GenAI) in cartographic processes presents exciting new opportunities. Traditional approaches to map design often struggle to balance accuracy with aesthetic appeal, but the emergence of multimodal large language models (MLLMs) opens up a new realm of possibilities.
CartoAgent, the novel framework proposed in this study, leverages the power of MLLMs to simulate key stages in cartographic practice, such as preparation, map design, and evaluation. By assigning different MLLMs as agents with specific roles, CartoAgent enables collaboration and discussion between these virtual entities to produce visually appealing and informative maps.
One of the most intriguing aspects of CartoAgent is its ability to separate style from geographic data, allowing for the creation of unique map styles without compromising geographic accuracy. This innovative approach to map restyling, demonstrated through map style transfer and evaluation tasks, showcases the potential of GenAI in revolutionizing cartography.
As an expert commentator in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, I see the multi-disciplinary nature of this research as a bridge between the realms of AI and cartography. The integration of GenAI in cartographic design decisions is a promising path towards more efficient and creative map-making processes.
Future advancements in CartoAgent could lead to even more sophisticated map design techniques and ultimately transform the way we interact with and interpret geographic information. This study sets the stage for further exploration and integration of GenAI in the field of cartography, offering a glimpse into the exciting possibilities that lie ahead.
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by jsendak | May 16, 2025 | Computer Science
Expert Commentary: Unveiling Vulnerabilities in Anonymized Speech Systems
The development of SpecWav-Attack, an adversarial model aimed at detecting speakers in anonymized speech, sheds light on the vulnerabilities present in current speech anonymization systems. By utilizing advanced techniques such as Wav2Vec2 for feature extraction, spectrogram resizing, and incremental training, SpecWav-Attack showcases superior performance compared to traditional attacks.
The evaluation of SpecWav-Attack on widely used datasets like librispeech-dev and librispeech-test indicates its ability to outperform conventional attacks, highlighting the critical need for enhanced defenses in anonymized speech systems. The results obtained from benchmarking against the ICASSP 2025 Attacker Challenge further emphasize the urgency for stronger security measures in place.
Insights and Future Directions
- Enhanced Defense Mechanisms: The success of SpecWav-Attack underscores the importance of developing robust defenses against adversarial attacks in speech anonymization. Future research efforts should focus on designing more resilient systems to safeguard user privacy and prevent speaker identification.
- Adversarial Training: Integrating adversarial training techniques into the model development process could potentially mitigate the effectiveness of attacks like SpecWav-Attack. By exposing the system to diverse adversarial examples during training, it can learn to better handle such threats in real-world scenarios.
- Ethical Considerations: As advancements in speaker detection technologies continue to evolve, ethical implications surrounding privacy and data security become paramount. Striking a balance between innovation and protecting user anonymity is essential for promoting trust and transparency in speech processing applications.
Overall, SpecWav-Attack serves as a wake-up call for the research community and industry stakeholders to reevaluate existing security measures in anonymized speech systems. By addressing the vulnerabilities brought to light by this adversarial model, we can pave the way for more secure and resilient technologies in the future.
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