by jsendak | Aug 7, 2024 | Computer Science
arXiv:2408.02978v1 Announce Type: new
Abstract: E-commerce is increasingly multimedia-enriched, with products exhibited in a broad-domain manner as images, short videos, or live stream promotions. A unified and vectorized cross-domain production representation is essential. Due to large intra-product variance and high inter-product similarity in the broad-domain scenario, a visual-only representation is inadequate. While Automatic Speech Recognition (ASR) text derived from the short or live-stream videos is readily accessible, how to de-noise the excessively noisy text for multimodal representation learning is mostly untouched. We propose ASR-enhanced Multimodal Product Representation Learning (AMPere). In order to extract product-specific information from the raw ASR text, AMPere uses an easy-to-implement LLM-based ASR text summarizer. The LLM-summarized text, together with visual data, is then fed into a multi-branch network to generate compact multimodal embeddings. Extensive experiments on a large-scale tri-domain dataset verify the effectiveness of AMPere in obtaining a unified multimodal product representation that clearly improves cross-domain product retrieval.
Expanding the Concept of Multimedia-Enriched E-commerce
E-commerce has evolved greatly in recent years, with products being showcased in various multimedia formats such as images, short videos, and even live stream promotions. This broad-domain approach allows for a more engaging and immersive shopping experience for consumers. However, to effectively represent these products across different domains, a unified and vectorized cross-domain production representation is crucial.
The challenge lies in the fact that there is often significant variation within products themselves, while still maintaining high similarity to other products in the same domain. Simply relying on visual representation alone is insufficient in this broad-domain scenario. This is where Automatic Speech Recognition (ASR) can play a crucial role.
ASR-Enhanced Multimodal Product Representation Learning (AMPere)
To address the limitations of visual-only representation, the proposed solution is ASR-enhanced Multimodal Product Representation Learning, or AMPere. The goal of AMPere is to utilize the readily accessible ASR text derived from short videos or live streams and leverage it to enhance the multimodal representation learning process. However, the challenge lies in de-noising the often noisy ASR text.
AMPere tackles this challenge by employing an easy-to-implement LLM-based ASR text summarizer, which effectively extracts product-specific information from the raw ASR text. This summarized text is then combined with the visual data and fed into a multi-branch network, resulting in the generation of compact multimodal embeddings.
The Importance of Cross-Domain Product Retrieval
Extensive experiments on a large-scale tri-domain dataset validate the effectiveness of AMPere in obtaining a unified multimodal product representation, which in turn improves cross-domain product retrieval. This is critical in the context of e-commerce, as it allows for more accurate and efficient product recommendations and search results.
The concepts discussed in this article highlight the multi-disciplinary nature of multimedia information systems. By combining elements from fields such as computer vision, natural language processing, and machine learning, AMPere offers a comprehensive approach to addressing the complexities of representing and retrieving products in a multimedia-enriched e-commerce environment.
Link to Other Related Fields
AMPere’s integration of ASR technology is directly related to the field of Artificial Reality (AR) and Augmented Reality (AR), as it enhances the immersive experience by intelligently incorporating text-based information into the representation of virtual products. Additionally, the focus on multimodal embeddings aligns with the broader field of Virtual Realities (VR), where the goal is to create realistic and interactive virtual environments.
In conclusion, the development of AMPere showcases the importance of a holistic, multi-disciplinary approach in advancing the capabilities of multimedia information systems within the realm of e-commerce. By effectively leveraging ASR technology and incorporating it into the learning process, AMPere takes a significant step towards achieving a unified and comprehensive representation of products in a multimedia-enriched e-commerce landscape.
Read the original article
by jsendak | Aug 7, 2024 | Computer Science
Intent Obfuscation: A New Frontier in Adversarial Attacks on Machine Learning Systems
Adversarial attacks on machine learning systems have become all too common in recent years, resulting in significant concerns about model security and reliability. These attacks involve manipulating the input to a machine learning model in such a way that it misclassifies or fails to detect the intended target object. However, a new and intriguing approach to adversarial attacks has emerged – intent obfuscation.
The Power of Intent Obfuscation
Intent obfuscation involves perturbing a non-overlapping object in an image to disrupt the detection of the target object, effectively hiding the attacker’s intended target. These adversarial examples, when fed into popular object detection models such as YOLOv3, SSD, RetinaNet, Faster R-CNN, and Cascade R-CNN, successfully manipulate the models and achieve the desired outcome.
The success of intent obfuscating attacks lies in the careful selection of the non-overlapping object to perturb, as well as its size and the confidence level of the target object. In our randomized experiment, we found that the larger the perturbed object and the higher the confidence level of the target object, the greater the success rate of the attack. This insight opens avenues for further research and development in designing effective adversarial attacks.
Exploiting Success Factors
Building upon the success of intent obfuscating attacks, it is possible for attackers to exploit the identified success factors to increase success rates across various models and attack types. By understanding the vulnerabilities and limitations of different object detectors, attackers can fine-tune their intent obfuscating techniques to maximize their impact.
Researchers and practitioners in the field of machine learning security must be aware of these advances in attack methodology to develop robust and resilient defense mechanisms. Defenses against intent obfuscation should prioritize understanding and modeling the attacker’s perspective, enabling the detection and mitigation of such attacks in real-time.
Legal Ramifications and Countermeasures
The rise of intent obfuscation in adversarial attacks raises important legal and ethical questions. As attackers employ tactics to avoid culpability, it is necessary for legal frameworks to adapt and address these novel challenges. The responsibility of securing machine learning models should not solely rest on the shoulders of developers but also requires strict regulations and standards that hold attackers accountable.
In addition to legal measures, robust countermeasures must be developed to protect machine learning systems from intent obfuscating attacks. These countermeasures should focus on continuously improving the security and resilience of models, integrating adversarial training techniques, and implementing proactive monitoring systems to detect and respond to new attack vectors.
Intent obfuscation marks a significant development in adversarial attacks on machine learning systems. Its potency and ability to evade detection highlight the need for proactive defense mechanisms and legal frameworks that can keep pace with the rapidly evolving landscape of AI security.
As researchers delve deeper into intent obfuscation and its implications, a deeper understanding of attack strategies and defense mechanisms will emerge. With increased collaboration between academia, industry, and policymakers, we can fortify our machine learning systems and ensure their robustness in the face of evolving adversarial threats.
Read the original article
by jsendak | Aug 6, 2024 | Computer Science
arXiv:2408.01651v1 Announce Type: new
Abstract: In today’s music industry, album cover design is as crucial as the music itself, reflecting the artist’s vision and brand. However, many AI-driven album cover services require subscriptions or technical expertise, limiting accessibility. To address these challenges, we developed Music2P, an open-source, multi-modal AI-driven tool that streamlines album cover creation, making it efficient, accessible, and cost-effective through Ngrok. Music2P automates the design process using techniques such as Bootstrapping Language Image Pre-training (BLIP), music-to-text conversion (LP-music-caps), image segmentation (LoRA), and album cover and QR code generation (ControlNet). This paper demonstrates the Music2P interface, details our application of these technologies, and outlines future improvements. Our ultimate goal is to provide a tool that empowers musicians and producers, especially those with limited resources or expertise, to create compelling album covers.
Expert Commentary: The Importance of Album Cover Design in the Music Industry
In the dynamic world of the music industry, album cover design plays a crucial role in capturing the essence of the music and reflecting the artist’s vision and brand. The visual representation of an album is often the first point of contact for potential listeners, conveying the mood and style of the music contained within.
However, creating album covers can be a daunting task for musicians and producers, especially those with limited resources or technical expertise. This is where AI-driven tools like Music2P come in, streamlining the album cover creation process and making it more accessible to a wider range of artists.
The Multi-Disciplinary Nature of Music2P
Music2P is a multi-modal AI-driven tool that harnesses various techniques to automate the design process of album covers. This makes it a prime example of how the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities can converge to enhance the music industry.
One of the key technologies utilized by Music2P is Bootstrapping Language Image Pre-training (BLIP), which enables the tool to generate album covers by analyzing the relationship between text and images. By using advanced natural language processing techniques, Music2P can understand the artist’s description or keywords and generate a visual representation that aligns with their vision.
Another important aspect of Music2P is its music-to-text conversion capability (LP-music-caps). This feature allows musicians to input their melodies or musical motifs and convert them into meaningful text descriptions. This not only assists in generating album covers but also helps in the overall branding process.
Additionally, Music2P incorporates image segmentation techniques (LoRA) to enhance the visual aesthetics of album covers. This enables the tool to identify various elements within an image and manipulate them to create visually appealing compositions. By leveraging these techniques, Music2P can ensure that the generated album covers are visually engaging and resonate with the target audience.
Furthermore, Music2P includes album cover and QR code generation capabilities through ControlNet. This allows musicians and producers to have complete control over the design and branding of their albums, ensuring that the final product is cohesive and professional-looking.
The Future of Music2P
While Music2P is already a powerful tool that empowers musicians and producers, the future holds great potential for its further improvement. Enhanced algorithms and neural networks can be integrated to refine the album cover generation process, resulting in even more personalized and compelling designs.
Addition of virtual reality (VR) and augmented reality (AR) features to Music2P can take album cover experience to the next level. Imagine being able to visualize and interact with album covers in a virtual or augmented environment, giving listeners a more immersive and memorable experience.
Furthermore, as the music industry continues to evolve, it is essential for Music2P to adapt to new trends and styles. The tool can incorporate machine learning models that learn from the constantly changing landscape of album designs, ensuring it remains up-to-date and relevant.
In conclusion, Music2P represents the intersection of multiple disciplines, combining the principles of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities to create a tool that revolutionizes album cover design. By providing an efficient, accessible, and cost-effective solution, Music2P empowers artists to bring their creative vision to life and captivate their audience.
Read the original article
by jsendak | Aug 6, 2024 | Computer Science
Artificial Intelligence (AI) and Internet of Things (IoT) technologies have revolutionized network communications, but they have also exposed the limitations of traditional Shannon-Nyquist theorem-based approaches. These traditional approaches neglect the semantic information within the transmitted content, making it difficult for receivers to extract the true meaning of the information.
To address this issue, the concept of Semantic Communication (SemCom) has emerged. SemCom focuses on extracting the underlying meaning from the transmitted content, allowing for more accurate and meaningful communication. The key to SemCom is the use of a shared knowledge base (KB) that helps receivers interpret the semantic information correctly.
This paper proposes a two-stage hierarchical qualification and validation model for natural language-based machine-to-machine (M2M) SemCom. This model can be applied in various applications, including autonomous driving and edge computing, where accurate and reliable communication is crucial.
In this model, the degree of understanding (DoU) between two communication parties is measured quantitatively at both the word and sentence levels. This quantification allows for a more precise assessment of the level of understanding between the parties. Furthermore, the DoU is validated and ensured at each level before moving on to the next step, ensuring a high level of accuracy in the communication process.
The effectiveness of this model has been tested and verified through a series of experiments. The results demonstrate that the proposed quantification and validation method significantly improves the DoU of inter-machine SemCom. This improvement is a crucial step towards achieving more accurate and meaningful communication in M2M scenarios.
This research has significant implications for the development of AI and IoT technologies. By addressing the limitations of traditional communication methods and focusing on semantic information, SemCom opens up new possibilities for more intelligent and context-aware communication between machines. This advancement can enhance applications such as autonomous driving, where machines need to understand and respond to complex situations in real-time.
In conclusion, the two-stage hierarchical qualification and validation model proposed in this paper represents an important step forward in improving machine-to-machine SemCom. By addressing the limitations of traditional communication approaches and emphasizing semantic information, this model brings us closer to achieving more accurate and meaningful communication in AI and IoT-enabled scenarios.
Read the original article
by jsendak | Aug 2, 2024 | Computer Science
arXiv:2408.00305v1 Announce Type: new
Abstract: Cross-modal coherence modeling is essential for intelligent systems to help them organize and structure information, thereby understanding and creating content of the physical world coherently like human-beings. Previous work on cross-modal coherence modeling attempted to leverage the order information from another modality to assist the coherence recovering of the target modality. Despite of the effectiveness, labeled associated coherency information is not always available and might be costly to acquire, making the cross-modal guidance hard to leverage. To tackle this challenge, this paper explores a new way to take advantage of cross-modal guidance without gold labels on coherency, and proposes the Weak Cross-Modal Guided Ordering (WeGO) model. More specifically, it leverages high-confidence predicted pairwise order in one modality as reference information to guide the coherence modeling in another. An iterative learning paradigm is further designed to jointly optimize the coherence modeling in two modalities with selected guidance from each other. The iterative cross-modal boosting also functions in inference to further enhance coherence prediction in each modality. Experimental results on two public datasets have demonstrated that the proposed method outperforms existing methods for cross-modal coherence modeling tasks. Major technical modules have been evaluated effective through ablation studies. Codes are available at: url{https://github.com/scvready123/IterWeGO}.
Cross-modal Coherence Modeling: Unlocking the Potential of Intelligent Systems
Intelligent systems have made tremendous progress in understanding and organizing information from the physical world. However, they still lag behind humans in terms of coherence and context understanding. One key challenge is modeling cross-modal coherence, which involves leveraging information from multiple modalities to create a coherent understanding of the world.
The article introduces the Weak Cross-Modal Guided Ordering (WeGO) model as a novel approach to cross-modal coherence modeling. Unlike previous methods that rely on labeled associated coherency information, WeGO leverages high-confidence predicted pairwise order in one modality as reference information to guide the coherence modeling in another modality. This allows the system to take advantage of cross-modal guidance without the need for expensive or unavailable gold labels on coherency.
Unlocking the Potential of Cross-modal Guidance
This new approach has significant implications for the field of multimedia information systems and related technologies such as animations, artificial reality, augmented reality, and virtual realities. Cross-modal coherence modeling is a multi-disciplinary concept that spans various domains, and WeGO opens up new possibilities for achieving coherence and context understanding in intelligent systems.
One of the key advantages of WeGO is its iterative learning paradigm, which optimizes coherence modeling in two modalities by incorporating selected guidance from each other. This iterative cross-modal boosting not only enhances coherence prediction during model training, but also improves inference performance by further enhancing coherence prediction in each modality. This iterative approach allows the system to continuously refine its understanding and coherence modeling abilities.
Practical Implications and Future Directions
The experimental results on two public datasets showcased the effectiveness of the WeGO model in comparison to existing methods. The major technical modules of WeGO were evaluated through ablation studies, further demonstrating their effectiveness in cross-modal coherence modeling tasks.
As we move forward, the WeGO model holds the potential to enhance various applications and systems that rely on cross-modal coherence, including intelligent assistants, content recommendation systems, and virtual reality experiences. Additionally, this research opens up new avenues for exploring the role of cross-modal guidance in the wider field of multimedia information systems.
In conclusion, the WeGO model represents a significant advancement in the field of cross-modal coherence modeling. By leveraging cross-modal guidance without the need for labeled coherency information, it unlocks greater potential for intelligent systems to understand and create content coherently, ultimately bridging the gap between human-like understanding and machine intelligence.
Read the original article
by jsendak | Aug 2, 2024 | Computer Science
Analysis of Visual Diffusion Models and Replication Phenomenon
The emergence of visual diffusion models has undoubtedly revolutionized the field of creative AI, enabling the generation of high-quality and diverse content. However, this advancement comes with significant concerns regarding privacy, security, and copyright, due to the inherent tendency of these models to memorize training images or videos and subsequently replicate their concepts, content, or styles during inference.
Unveiling Replication Instances
One important aspect covered in this survey is the methods used to detect replication instances, a process we refer to as “unveiling.” By categorizing and analyzing existing studies, the authors have contributed to our understanding of the different techniques employed to identify instances of replication. This knowledge is crucial for further research and the development of effective countermeasures.
Understanding the Phenomenon
Understanding the underlying mechanisms and factors that contribute to replication is another key aspect explored in this survey. By delving into the intricacies of visual diffusion models, the authors shed light on the processes that lead to replication and provide valuable insights for future research. This understanding can aid in the development of strategies to mitigate or potentially prevent replication in the first place.
Mitigating Replication
The survey also highlights the importance of mitigating replication and discusses various strategies to achieve this goal. By focusing on the development of techniques that can reduce or eliminate replication, researchers can address the aforementioned concerns related to privacy, security, and copyright infringement. This section of the survey provides a valuable resource for researchers and practitioners aiming to create more responsible and ethically aligned AI systems.
Real-World Influence and Challenges
Beyond the technical aspects of replication, the survey explores the real-world influence of this phenomenon. In sectors like healthcare, where privacy concerns regarding patient data are paramount, replication becomes a critical issue. By examining the implications of replication in specific domains, the authors broaden the scope of the survey and highlight the urgency of finding robust mitigation strategies.
Furthermore, the survey acknowledges the ongoing challenges in this field, including the difficulty in detecting and benchmarking replication. These challenges are crucial to address to ensure the effectiveness of mitigation techniques and the progress of research in this area.
Future Directions
The survey concludes by outlining future directions for research, emphasizing the need for more robust mitigation techniques. It highlights the importance of continued innovation in developing strategies to counter replication and maintain the integrity, privacy, and security of AI-generated content. By synthesizing insights from diverse studies, this survey equips researchers and practitioners with a deeper understanding of the intersection between AI technology and social good.
This comprehensive review contributes significantly to the field of visual diffusion models and replication. It not only categorizes and analyzes existing studies but also addresses real-world implications and outlines future directions. Researchers and practitioners can use this survey as a valuable resource to inform their work and contribute to the responsible development of AI systems.
For more details, the project can be accessed here.
Read the original article
