by jsendak | Aug 1, 2024 | Computer Science
arXiv:2407.21721v1 Announce Type: new
Abstract: Audio-visual semantic segmentation (AVSS) aims to segment and classify sounding objects in videos with acoustic cues. However, most approaches operate on the close-set assumption and only identify pre-defined categories from training data, lacking the generalization ability to detect novel categories in practical applications. In this paper, we introduce a new task: open-vocabulary audio-visual semantic segmentation, extending AVSS task to open-world scenarios beyond the annotated label space. This is a more challenging task that requires recognizing all categories, even those that have never been seen nor heard during training. Moreover, we propose the first open-vocabulary AVSS framework, OV-AVSS, which mainly consists of two parts: 1) a universal sound source localization module to perform audio-visual fusion and locate all potential sounding objects and 2) an open-vocabulary classification module to predict categories with the help of the prior knowledge from large-scale pre-trained vision-language models. To properly evaluate the open-vocabulary AVSS, we split zero-shot training and testing subsets based on the AVSBench-semantic benchmark, namely AVSBench-OV. Extensive experiments demonstrate the strong segmentation and zero-shot generalization ability of our model on all categories. On the AVSBench-OV dataset, OV-AVSS achieves 55.43% mIoU on base categories and 29.14% mIoU on novel categories, exceeding the state-of-the-art zero-shot method by 41.88%/20.61% and open-vocabulary method by 10.2%/11.6%. The code is available at https://github.com/ruohaoguo/ovavss.
Expert Commentary: Open-Vocabulary Audio-Visual Semantic Segmentation
In the field of multimedia information systems, audio-visual semantic segmentation (AVSS) plays a significant role in understanding and processing audio and visual content in videos. Traditionally, AVSS approaches have focused on identifying and classifying pre-defined categories based on training data. However, in practical applications, it is essential to have the ability to detect and recognize novel categories that may not be present in the training data. This is where the concept of open-vocabulary AVSS comes into play.
Open-Vocabulary AVSS: A Challenging Task
Open-vocabulary audio-visual semantic segmentation extends the capabilities of AVSS to handle open-world scenarios beyond the annotated label space. It involves recognizing and segmenting all categories, including those that have never been seen or heard during training. This task is highly challenging as it requires a model to generalize and adapt to new categories without any prior knowledge.
The OV-AVSS Framework
The authors of this paper propose the first open-vocabulary AVSS framework called OV-AVSS. This framework consists of two main components:
- Universal sound source localization module: This module performs audio-visual fusion and locates all potential sounding objects in the video. It combines information from both auditory and visual cues to improve localization accuracy.
- Open-vocabulary classification module: This module predicts categories using prior knowledge from large-scale pre-trained vision-language models. It leverages the power of pre-trained models to generalize and recognize novel categories in an open-vocabulary setting.
Evaluation and Results
To evaluate the performance of the proposed open-vocabulary AVSS framework, the authors introduce the AVSBench-OV dataset. This dataset includes split zero-shot training and testing subsets and serves as a benchmark for open-vocabulary AVSS. The experiments conducted on this dataset demonstrate the strong segmentation and zero-shot generalization ability of the OV-AVSS model.
On the AVSBench-OV dataset, OV-AVSS achieves 55.43% mIoU (mean intersection over union) on base categories and 29.14% mIoU on novel categories. These results surpass the state-of-the-art zero-shot method by 41.88% (base categories) and 20.61% (novel categories), as well as the open-vocabulary method by 10.2% (base categories) and 11.6% (novel categories).
Implications and Future Directions
The concept of open-vocabulary audio-visual semantic segmentation has implications for a wide range of multimedia information systems. As the field progresses, the ability to recognize and segment novel categories without prior training data will become increasingly valuable in practical applications. Additionally, the integration of audio and visual cues, as demonstrated in the OV-AVSS framework, highlights the multidisciplinary nature of the concepts within AVSS and its related fields such as animations, artificial reality, augmented reality, and virtual realities.
In the future, further research can explore the development of more advanced open-vocabulary AVSS models and datasets to push the boundaries of zero-shot generalization and enable practical applications in real-world scenarios. The availability of the code for the OV-AVSS framework on GitHub provides a valuable resource for researchers and practitioners interested in advancing the field.
Read the original article
by jsendak | Aug 1, 2024 | Computer Science
Expert Commentary: The Importance of the Retrieval Stage in Recommender Systems
In today’s digital age, with an overwhelming amount of data available across various platforms, recommender systems play a crucial role in helping users navigate through the information overload. Multi-stage cascade ranking systems have emerged as the industry standard, with retrieval and ranking being the two main stages of these systems.
While significant attention has been given to the ranking stage, this survey sheds light on the often overlooked retrieval stage of recommender systems. The retrieval stage involves sifting through a large number of candidates to filter out irrelevant items, and it lays the foundation for an effective recommendation system.
Improving Similarity Computation
One key area of focus in enhancing retrieval is improving similarity computation between users and items. Recommender systems rely on calculating the similarity between user preferences and item descriptions to find relevant recommendations. This survey explores different techniques and algorithms to make similarity computation more accurate and effective. By improving the computation of similarity, recommender systems can provide more precise recommendations that align with users’ preferences.
Enhancing Indexing Mechanisms
Efficient retrieval is another critical aspect of recommender systems. To achieve this, indexing mechanisms need to be optimized to handle large datasets and facilitate fast retrieval of relevant items. This survey examines various indexing mechanisms and explores how they can be enhanced to improve the efficiency of the retrieval stage. By implementing efficient indexing mechanisms, recommender systems can quickly retrieve relevant items, resulting in a better user experience.
Optimizing Training Methods
The training methods used for retrieval play a significant role in the performance of recommender systems. This survey reviews different training methods and analyzes their impact on retrieval accuracy and efficiency. By optimizing training methods, recommender systems can ensure the retrieval stage is both precise and efficient, providing users with highly relevant recommendations in a timely manner.
Benchmarking Experiments and Case Study
To evaluate the effectiveness of various techniques and approaches in the retrieval stage, this survey includes a comprehensive set of benchmarking experiments conducted on three public datasets. These experiments provide valuable insights into the performance of different retrieval methods and their applicability in real-world scenarios.
The survey also features a case study on retrieval practices at a specific company, offering insights into the retrieval process and online serving. By showcasing real-world examples, this case study highlights the practical implications and challenges involved in implementing retrieval in recommender systems in the industry.
Building a Foundation for Optimizing Recommender Systems
By focusing on the retrieval stage, this survey aims to bridge the existing knowledge gap and serve as a cornerstone for researchers interested in optimizing this critical component of cascade recommender systems. The retrieval stage is fundamental for effective recommendations, and by improving its accuracy, efficiency, and training methods, recommender systems can enhance user satisfaction and engagement.
In conclusion, this survey emphasizes the importance of the retrieval stage in recommender systems, providing a comprehensive analysis of existing work and current practices. By addressing key areas such as similarity computation, indexing mechanisms, and training methods, researchers and practitioners can further optimize this critical component of cascade recommender systems, ultimately benefiting users in navigating through the vast sea of digital information.
Read the original article
by jsendak | Jul 31, 2024 | Computer Science
arXiv:2407.20337v1 Announce Type: cross
Abstract: Discerning between authentic content and that generated by advanced AI methods has become increasingly challenging. While previous research primarily addresses the detection of fake faces, the identification of generated natural images has only recently surfaced. This prompted the recent exploration of solutions that employ foundation vision-and-language models, like CLIP. However, the CLIP embedding space is optimized for global image-to-text alignment and is not inherently designed for deepfake detection, neglecting the potential benefits of tailored training and local image features. In this study, we propose CoDE (Contrastive Deepfake Embeddings), a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities. To sustain the training of our model, we generate a comprehensive dataset that focuses on images generated by diffusion models and encompasses a collection of 9.2 million images produced by using four different generators. Experimental results demonstrate that CoDE achieves state-of-the-art accuracy on the newly collected dataset, while also showing excellent generalization capabilities to unseen image generators. Our source code, trained models, and collected dataset are publicly available at: https://github.com/aimagelab/CoDE.
Analysis of CoDE: A Novel Embedding Space for Deepfake Detection
Deepfake technology has become increasingly sophisticated, making it challenging to discern between authentic content and AI-generated fake images. While previous research has primarily focused on detecting fake faces, identifying generated natural images has recently emerged as a new area of study. In response to this, the development of solutions that utilize foundation vision-and-language models, such as CLIP, has gained traction.
However, the authors of this study argue that the CLIP embedding space, while effective for global image-to-text alignment, is not specifically optimized for deepfake detection. They propose a novel embedding space called CoDE (Contrastive Deepfake Embeddings), which is designed to address the limitations of CLIP.
CoDE is trained through contrastive learning, a method that encourages the model to learn similarities between different global-local image features. By incorporating this approach, the researchers aim to enhance the detection of deepfake images. To train the CoDE model, they generate a comprehensive dataset consisting of 9.2 million images produced by four different generators that utilize diffusion models.
The experimental results demonstrate that CoDE achieves state-of-the-art accuracy on the newly collected dataset. Additionally, the model exhibits excellent generalization capabilities to unseen image generators. This highlights the effectiveness of CoDE as a specialized embedding space tailored for deepfake detection.
The significance of this study lies in its multi-disciplinary nature, combining concepts from computer vision, natural language processing, and machine learning. By leveraging the knowledge and techniques from these fields, the authors have developed a powerful tool that contributes to the growing field of multimedia information systems.
CoDE’s implications extend beyond deepfake detection. As deepfake technology continues to advance, it becomes crucial to develop specialized tools and models that can discern between authentic and manipulated content across various domains, including animations, artificial reality, augmented reality, and virtual realities.
In the context of multimedia information systems, CoDE can aid in the development of robust and reliable systems that automatically detect and filter out deepfake content. This is particularly relevant for platforms that rely on user-generated content, such as social media platforms, online video sharing platforms, and news outlets.
Furthermore, CoDE’s potential reaches into the realms of animations, artificial reality, augmented reality, and virtual realities. These technologies heavily rely on generating realistic and immersive visual experiences. By incorporating CoDE or similar techniques, the risk of fake or manipulated content within these domains can be mitigated, ensuring a more authentic and trustworthy user experience.
In conclusion, CoDE presents a significant advancement in the field of deepfake detection, offering a specialized embedding space that outperforms previous approaches. Its multi-disciplinary nature demonstrates the intersectionality of computer vision, natural language processing, and machine learning. As deepfake technology evolves, further advancements in the detection and mitigation of fake content will be necessary across various multimedia domains, and CoDE paves the way for such developments.
Read the original article
by jsendak | Jul 31, 2024 | Computer Science
Promising Prospects and Potential Hurdles for MentorAI: An Analysis
MentorAI, a hypothetical AI-driven mentorship platform, has generated significant interest due to its promising prospects in revolutionizing professional growth and development. However, the realization of this platform also brings forth several potential hurdles that need careful consideration. In this article, we delve into the essential characteristics, technological underpinnings, transformative potential, and associated challenges of MentorAI.
Essential Characteristics and Technological Underpinnings
MentorAI aims to provide tailored mentorship experiences by leveraging artificial intelligence (AI) technologies. This approach allows the platform to offer real-time guidance, resources, and assistance that are customized to each individual’s specific needs and goals. By utilizing machine learning and natural language comprehension, MentorAI can process user inputs, deliver context-sensitive responses, and dynamically adjust to user preferences and objectives.
This ability to adapt and learn from user interactions is crucial for delivering a personalized mentoring experience. It enables MentorAI to understand the unique challenges and aspirations of individuals, ensuring that the guidance and resources provided are relevant and effective.
Transformative Potential of MentorAI
The transformative potential of MentorAI on professional growth is immense. By providing personalized mentorship, this platform can help boost career progression by addressing skill gaps and guiding individuals towards suitable opportunities. Moreover, MentorAI’s support in skill development can empower professionals to acquire new competencies and stay relevant in a rapidly evolving work environment.
Additionally, MentorAI can play a vital role in supporting a balanced work-life environment. Through its AI-driven approach, individuals can receive guidance on time management, work-life integration, and stress reduction, ensuring they thrive both professionally and personally.
Potential Challenges and Ethical Concerns
While the development and deployment of MentorAI offer exciting possibilities, it is important to acknowledge and address potential challenges and ethical concerns. Data protection and security are critical issues, as MentorAI will likely gather and analyze vast amounts of personal information. Safeguarding this data and ensuring user privacy must be a top priority to maintain trust and prevent misuse.
Another challenge lies in algorithmic bias. AI systems like MentorAI rely on algorithms, and if these algorithms are biased, they can perpetuate inequality or discriminatory practices. Developing unbiased algorithms and continuously monitoring and auditing the system’s outputs are crucial steps to ensure fairness and inclusivity in the mentoring process.
Moreover, the idea of substituting human mentors with AI systems raises moral quandaries. While AI can offer valuable guidance and resources, it may lack the empathy and context awareness that human mentors provide. Finding the right balance between AI-driven mentorship and human interaction is vital to avoid a complete erosion of human touch and connection.
Conclusion
In conclusion, the development of the MentorAI platform shows immense promise for transforming professional growth and development. Its personalized and adaptive approach, empowered by AI technologies, can significantly enhance career progression, skill development, and work-life balance. However, challenges such as data protection, algorithmic bias, and the potential loss of human connection must be proactively addressed to ensure a positive impact on users. By carefully navigating these hurdles, MentorAI can emerge as a powerful tool in fostering professional growth in the digital era.
Read the original article
by jsendak | Jul 30, 2024 | Computer Science
arXiv:2407.19415v1 Announce Type: new
Abstract: The burgeoning short video industry has accelerated the advancement of video-music retrieval technology, assisting content creators in selecting appropriate music for their videos. In self-supervised training for video-to-music retrieval, the video and music samples in the dataset are separated from the same video work, so they are all one-to-one matches. This does not match the real situation. In reality, a video can use different music as background music, and a music can be used as background music for different videos. Many videos and music that are not in a pair may be compatible, leading to false negative noise in the dataset. A novel inter-intra modal (II) loss is proposed as a solution. By reducing the variation of feature distribution within the two modalities before and after the encoder, II loss can reduce the model’s overfitting to such noise without removing it in a costly and laborious way. The video-music retrieval framework, II-CLVM (Contrastive Learning for Video-Music Retrieval), incorporating the II Loss, achieves state-of-the-art performance on the YouTube8M dataset. The framework II-CLVTM shows better performance when retrieving music using multi-modal video information (such as text in videos). Experiments are designed to show that II loss can effectively alleviate the problem of false negative noise in retrieval tasks. Experiments also show that II loss improves various self-supervised and supervised uni-modal and cross-modal retrieval tasks, and can obtain good retrieval models with a small amount of training samples.
Analysis: The Advancement of Video-Music Retrieval Technology
In the rapidly growing short video industry, selecting appropriate music for videos is a crucial task for content creators. The development of video-music retrieval technology has greatly assisted in this process. However, the current self-supervised training methods for video-to-music retrieval have certain limitations that do not accurately reflect real-life scenarios.
In self-supervised training, the video and music samples in the dataset are matched one-to-one from the same video work. Unfortunately, this approach fails to account for the fact that a video can have different background music options, and a piece of music can be used as background music for multiple videos. As a result, there may be many compatible video-music combinations that are not included in the dataset, leading to false negative noise.
Multi-disciplinary Nature
The proposed solution to address this issue introduces a novel inter-intra modal (II) loss. This loss aims to reduce the variation of feature distribution within the two modalities (video and music) both before and after encoding. By doing so, the II loss can decrease the model’s overfitting to false negative noise without the need for expensive and laborious removal methods.
The introduction of the II-CLVM framework (Contrastive Learning for Video-Music Retrieval) incorporating the II Loss has demonstrated state-of-the-art performance on the YouTube8M dataset. This framework shows particular promise in retrieving music using multi-modal video information, such as text in videos. The experiments conducted provide evidence that the II loss effectively alleviates the problem of false negative noise in retrieval tasks.
Moreover, the experiments also showcase the benefits of II loss in improving various self-supervised and supervised uni-modal and cross-modal retrieval tasks. This highlights the multi-disciplinary nature of the concepts discussed in this study.
Relation to Multimedia Information Systems and AR/VR
The concept of video-music retrieval technology intersects with the wider field of multimedia information systems. Multimedia information systems deal with the management, organization, and retrieval of multimedia data. The advancement of video-music retrieval contributes to the development of efficient systems for organizing and retrieving multimedia content based on audio features.
Furthermore, the article does not explicitly mention animations, artificial reality, augmented reality, and virtual realities. However, it is important to note that advancements in video-music retrieval technology can greatly enhance the immersive experiences in these domains. For example, in virtual reality applications, the ability to tailor music to specific scenarios or interactions can significantly enhance the overall user experience and immersion. The integration of video-music retrieval technologies with augmented reality can also lead to more interactive and personalized experiences, where the music adjusts based on the user’s actions or the environment.
Conclusion
The advancement of video-music retrieval technology, particularly with the introduction of the novel II loss and the II-CLVM framework, presents exciting possibilities for content creators and multimedia information systems. By addressing the limitations of current self-supervised training methods, this research contributes to improving the accuracy and efficiency of matching appropriate music to videos. The multi-disciplinary nature of these concepts highlights their relevance to the wider fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Read the original article
by jsendak | Jul 30, 2024 | Computer Science
Expert Commentary: Enhancing Spiking Neural Networks with Learnable Delays and Dynamic Pruning
Spiking Neural Networks (SNNs) have become increasingly popular in the field of neuromorphic computing due to their closer resemblance to biological neural networks. In this article, the authors present a model that incorporates two key enhancements – learnable synaptic delays and dynamic pruning – to improve the efficiency and biological realism of SNNs for temporal data processing.
Learnable Synaptic Delays using Dilated Convolution with Learnable Spacings (DCLS)
Synaptic delays play a crucial role in information processing in the brain, allowing for the sequential propagation of signals. The authors introduce a novel approach called Dilated Convolution with Learnable Spacings (DCLS) to incorporate learnable delays in their SNN model. By training the model on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time, they demonstrate that the network learns to utilize specific delays to improve its performance on temporal data tasks.
This approach has important implications for real-world applications that involve processing time-varying data, such as speech or video processing. By enabling SNNs to learn and adapt their synaptic delays, the model becomes more capable of capturing the spatio-temporal patterns present in the data, leading to improved accuracy and robustness.
Dynamic Pruning with DEEP R and RigL
To ensure optimal connectivity throughout training, the authors introduce a dynamic pruning strategy that combines DEEP R for connection removal and RigL for connection reintroduction. Pruning refers to the selective removal of connections in a neural network, reducing its computational and memory requirements while maintaining its performance. By dynamically pruning and rewiring the network, the model adapts to the task at hand and achieves a more efficient representation of the data.
This pruning strategy is particularly valuable in the context of SNNs, as it allows for the creation of networks with optimal connectivity, mimicking the sparse and selective connectivity observed in biological neural networks. By reducing the number of connections, the model becomes more biologically plausible and potentially more efficient in terms of energy consumption.
Enforcing Dale’s Principle for Excitation and Inhibition
Dale’s Principle states that individual neurons are either exclusively excitatory or inhibitory, but not both. By incorporating this principle into their SNN model, the authors align their model closer to biological neural networks, enhancing its biological realism. This constraint ensures that the network exhibits clear spatio-temporal patterns of excitation and inhibition after training.
The results of this research are significant as they shed light on the spatio-temporal dynamics in SNNs and demonstrate the robustness of the emerging patterns to both pruning and rewiring processes. This finding provides a solid foundation for future work in the field of neuromorphic computing and opens up exciting possibilities for developing efficient and biologically realistic SNN models for various applications.
In conclusion, the integration of learnable synaptic delays, dynamic pruning, and biological constraints presented in this article is a significant step towards enhancing the efficacy and biological realism of SNNs for temporal data processing. These advancements contribute to the development of more efficient and adaptive neuromorphic computing systems that can better process and understand time-varying information.
Read the original article
