by jsendak | May 15, 2024 | Computer Science
arXiv:2405.07229v1 Announce Type: new
Abstract: The rising popularity of multimodal large language models (MLLMs) has sparked a significant increase in research dedicated to evaluating these models. However, current evaluation studies predominantly concentrate on the ability of models to comprehend and reason within a unimodal (vision-only) context, overlooking critical performance evaluations in complex multimodal reasoning tasks that integrate both visual and text contexts. Furthermore, tasks that demand reasoning across multiple modalities pose greater challenges and require a deep understanding of multimodal contexts. In this paper, we introduce a comprehensive assessment framework named MM-InstructEval, which integrates a diverse array of metrics to provide an extensive evaluation of the performance of various models and instructions across a broad range of multimodal reasoning tasks with vision-text contexts. MM-InstructEval enhances the research on the performance of MLLMs in complex multimodal reasoning tasks, facilitating a more thorough and holistic zero-shot evaluation of MLLMs. We firstly utilize the “Best Performance” metric to determine the upper performance limit of each model across various datasets. The “Mean Relative Gain” metric provides an analysis of the overall performance across different models and instructions, while the “Stability” metric evaluates their sensitivity to variations. Historically, the research has focused on evaluating models independently or solely assessing instructions, overlooking the interplay between models and instructions. To address this gap, we introduce the “Adaptability” metric, designed to quantify the degree of adaptability between models and instructions. Evaluations are conducted on 31 models (23 MLLMs) across 16 multimodal datasets, covering 6 tasks, with 10 distinct instructions. The extensive analysis enables us to derive novel insights.
As the field of multimodal large language models (MLLMs) continues to advance, there is a growing need for comprehensive evaluation frameworks that can assess the performance of these models in complex multimodal reasoning tasks. The MM-InstructEval framework introduced in this paper aims to fill this gap by providing a diverse set of metrics to evaluate the performance of MLLMs across a broad range of tasks that integrate both visual and text contexts.
Multi-disciplinary Nature
The concepts discussed in this paper have a multi-disciplinary nature, spanning multiple fields such as natural language processing, computer vision, and human-computer interaction. By evaluating the performance of MLLMs in multimodal reasoning tasks, this research contributes to the development of more advanced and comprehensive multimedia information systems. These systems can utilize both textual and visual information to facilitate better understanding, decision-making, and interaction between humans and machines.
Related to Multimedia Information Systems
The MM-InstructEval framework is directly related to the field of multimedia information systems. These systems deal with the retrieval, management, and analysis of multimedia data, including text, images, and videos. By evaluating the performance of MLLMs in multimodal reasoning tasks, this framework enables the development of more effective multimedia information systems that can understand and reason over diverse modalities of data, improving the accuracy and usefulness of information retrieval and analysis tasks.
Related to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The evaluation of MLLMs in multimodal reasoning tasks has implications for various aspects of animations, artificial reality, augmented reality, and virtual realities. These technologies often rely on both visual and textual information to create immersive and interactive experiences. By improving the performance of MLLMs in understanding and reasoning across multimodal contexts, the MM-InstructEval framework can enhance the quality and realism of animations, artificial reality simulations, and augmented reality applications. It can also enable more intelligent virtual reality environments that can understand and respond to user instructions and queries more accurately and effectively.
Novel Insights from the Evaluation
The extensive analysis conducted using the MM-InstructEval framework on 31 models across 16 multimodal datasets and 6 tasks provides novel insights into the performance of MLLMs in complex reasoning tasks. The “Best Performance” metric helps determine the upper performance limit of each model, giving a baseline for comparison. The “Mean Relative Gain” metric provides an overall analysis of performance across different models and instructions, highlighting the strengths and weaknesses of each. The “Stability” metric evaluates the models’ sensitivity to variations, ensuring robustness. Lastly, the “Adaptability” metric measures the degree of adaptability between models and instructions, shedding light on the interplay between them.
By considering these metrics and conducting a comprehensive evaluation, researchers and developers can better understand the capabilities and limitations of MLLMs in multimodal reasoning tasks. This knowledge can inform the development of more advanced MLLMs, as well as the design and implementation of multimedia information systems, animations, artificial reality experiences, augmented reality applications, and virtual reality environments.
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by jsendak | Apr 26, 2024 | Computer Science
arXiv:2404.16305v1 Announce Type: new
Abstract: Existing works have made strides in video generation, but the lack of sound effects (SFX) and background music (BGM) hinders a complete and immersive viewer experience. We introduce a novel semantically consistent v ideo-to-audio generation framework, namely SVA, which automatically generates audio semantically consistent with the given video content. The framework harnesses the power of multimodal large language model (MLLM) to understand video semantics from a key frame and generate creative audio schemes, which are then utilized as prompts for text-to-audio models, resulting in video-to-audio generation with natural language as an interface. We show the satisfactory performance of SVA through case study and discuss the limitations along with the future research direction. The project page is available at https://huiz-a.github.io/audio4video.github.io/.
Improving the Immersive Experience with Video-to-Audio Generation
In the field of multimedia information systems, the combination of audio and visual elements plays a crucial role in creating an immersive viewer experience. While existing works have made significant strides in video generation, there has been a lack of attention to the inclusion of sound effects (SFX) and background music (BGM) in the generated videos. This omission hinders the creation of a complete and truly immersive viewer experience.
To address this limitation, a novel framework called SVA (Semantically-consistent Video-to-Audio generation) has been introduced. The primary objective of SVA is to automatically generate audio that is semantically consistent with the given video content. By harnessing the power of multimodal large language models (MLLM), SVA is able to understand the semantics of a video from its key frame and generate creative audio schemes that correspond to it.
The use of multimodal language models is significant in highlighting the multi-disciplinary nature of this research. It brings together concepts from natural language processing, computer vision, and audio processing to create an integrated framework that addresses a gap in the existing video generation techniques.
SVA makes use of prompts generated by the MLLM to drive text-to-audio models. These text-to-audio models then generate the final audio that is synchronized with the video content. The natural language interface provided by the prompts allows for intuitive control over the audio generation process.
The successful implementation of SVA has been demonstrated through a case study, which showcases the satisfactory performance of the framework. By generating audio that is semantically consistent with the video, SVA enhances the overall viewer experience, making it more immersive and engaging.
Looking ahead, the limitations and future research directions of the SVA framework need to be explored. For instance, how can the generation of audio be further enhanced to capture more fine-grained details of the video content? Additionally, the integration of SVA with emerging technologies such as augmented reality (AR) and virtual reality (VR) could open up new possibilities for creating highly immersive multimedia experiences.
In conclusion, the introduction of the SVA framework represents a significant advancement in the field of multimedia information systems. By automatically generating semantically consistent audio for videos, SVA contributes to the creation of more immersive and engaging viewer experiences. Its multi-disciplinary nature, combining concepts from natural language processing, computer vision, and audio processing, highlights the importance of integrating multiple domains for the advancement of multimedia technologies.
You can learn more about the SVA framework and access the project page here.
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by jsendak | Apr 3, 2024 | AI
The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music. However, due…
to the complex nature of music and the lack of standardized evaluation metrics, developing such benchmarks has proven to be a challenging task. In this article, we delve into the pressing need for new benchmarks to assess the capabilities of multimodal LLMs in understanding and describing music. As these models continue to advance at an unprecedented pace, it becomes crucial to have standardized measures that can comprehensively evaluate their performance. We explore the obstacles faced in creating these benchmarks and discuss potential solutions that can drive the development of improved evaluation metrics. By addressing this critical issue, we aim to pave the way for advancements in multimodal LLMs and their application in the realm of music understanding and description.
Proposing New Benchmarks for Evaluating Multimodal Large Language Models
Proposing New Benchmarks for Evaluating Multimodal Large Language Models
The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music. However, due to the complexity and subjective nature of musical comprehension, traditional evaluation methods often fall short in providing consistent and accurate assessments.
Music is a multifaceted art form that encompasses various structured patterns, emotional expressions, and unique interpretations. Evaluating an LLM’s understanding and description of music should consider these elements holistically. Instead of relying solely on quantitative metrics, a more comprehensive evaluation approach is needed to gauge the model’s ability to comprehend and convey the essence of music through text.
Multimodal Evaluation Benchmarks
To address the current evaluation gap, it is essential to design new benchmarks that combine both quantitative and qualitative measures. These benchmarks can be categorized into three main areas:
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Appreciation of Musical Structure: LLMs should be evaluated on their understanding of various musical components such as melody, rhythm, harmony, and form. Assessing their ability to describe these elements accurately and with contextual knowledge would provide valuable insights into the model’s comprehension capabilities.
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Emotional Representation: Music evokes emotions, and a successful LLM should be able to capture and describe the emotions conveyed by a piece of music effectively. Developing benchmarks that evaluate the model’s emotional comprehension and its ability to articulate these emotions in descriptive text can provide a deeper understanding of its capabilities.
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Creative Interpretation: Music interpretation is subjective, and different listeners may have unique perspectives on a musical piece. Evaluating an LLM’s capacity to generate diverse and creative descriptions that encompass various interpretations of a given piece can offer insights into its flexibility and intelligence.
By combining these benchmarks, a more holistic evaluation of multimodal LLMs can be achieved. It is crucial to involve experts from the fields of musicology, linguistics, and artificial intelligence to develop these benchmarks collaboratively, ensuring the assessments are comprehensive and accurate.
Importance of User Feedback
While benchmarks provide objective evaluation measures, it is equally important to gather user feedback and subjective opinions to assess the effectiveness and usability of multimodal LLMs in real-world applications. User studies, surveys, and focus groups can provide valuable insights into how well these models meet the needs and expectations of their intended audience.
“To unlock the full potential of multimodal LLMs, we must develop benchmarks that go beyond quantitative metrics and account for the nuanced understanding of music. Incorporating subjective evaluations and user feedback is key to ensuring these models have practical applications in enhancing music experiences.”
As the development of multimodal LLMs progresses, ongoing refinement and updating of the evaluation benchmarks will be necessary to keep up with the evolving capabilities of these models. Continued collaboration between researchers, practitioners, and music enthusiasts is pivotal in establishing a standard framework that can guide the development, evaluation, and application of multimodal LLMs in the music domain.
to the complex and subjective nature of music, creating a comprehensive benchmark for evaluating LLMs’ understanding and description of music poses a significant challenge. Music is a multifaceted art form that encompasses various elements such as melody, rhythm, harmony, lyrics, and emotional expression, making it inherently difficult to quantify and evaluate.
One of the primary obstacles in benchmarking LLMs for music understanding is the lack of a standardized dataset that covers a wide range of musical genres, styles, and cultural contexts. Existing datasets often focus on specific genres or limited musical aspects, which hinders the development of a holistic evaluation framework. To address this, researchers and experts in the field need to collaborate and curate a diverse and inclusive dataset that represents the vast musical landscape.
Another critical aspect to consider is the evaluation metrics for LLMs’ music understanding. Traditional metrics like accuracy or perplexity may not be sufficient to capture the nuanced nature of music. Music comprehension involves not only understanding the lyrics but also interpreting the emotional context, capturing the stylistic elements, and recognizing cultural references. Developing novel evaluation metrics that encompass these aspects is crucial to accurately assess LLMs’ performance in music understanding.
Furthermore, LLMs’ ability to textually describe music requires a deeper understanding of the underlying musical structure and aesthetics. While LLMs have shown promising results in generating descriptive text, there is still room for improvement. Future benchmarks should focus on evaluating LLMs’ capacity to generate coherent and contextually relevant descriptions that capture the essence of different musical genres and evoke the intended emotions.
To overcome these challenges, interdisciplinary collaborations between experts in natural language processing, music theory, and cognitive psychology are essential. By combining their expertise, researchers can develop comprehensive benchmarks that not only evaluate LLMs’ performance but also shed light on the limitations and areas for improvement.
Looking ahead, advancements in multimodal learning techniques, such as incorporating audio and visual information alongside textual data, hold great potential for enhancing LLMs’ understanding and description of music. Integrating these modalities can provide a more holistic representation of music and enable LLMs to capture the intricate interplay between lyrics, melody, rhythm, and emotions. Consequently, future benchmarks should consider incorporating multimodal data to evaluate LLMs’ performance comprehensively.
In summary, the rapidly evolving multimodal LLMs require new benchmarks to evaluate their understanding and textual description of music. Overcoming the challenges posed by the complex and subjective nature of music, the lack of standardized datasets, and the need for novel evaluation metrics will be crucial. Interdisciplinary collaborations and the integration of multimodal learning techniques hold the key to advancing LLMs’ capabilities in music understanding and description. By addressing these issues, we can pave the way for LLMs to become powerful tools for analyzing and describing music in diverse contexts.
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by jsendak | Mar 22, 2024 | AI
arXiv:2403.14077v1 Announce Type: new
Abstract: DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation. Detecting DeepFakes is currently solved with programmed machine learning algorithms. In this work, we investigate the capabilities of multimodal large language models (LLMs) in DeepFake detection. We conducted qualitative and quantitative experiments to demonstrate multimodal LLMs and show that they can expose AI-generated images through careful experimental design and prompt engineering. This is interesting, considering that LLMs are not inherently tailored for media forensic tasks, and the process does not require programming. We discuss the limitations of multimodal LLMs for these tasks and suggest possible improvements.
Investigating the Capabilities of Multimodal Large Language Models (LLMs) in DeepFake Detection
DeepFakes, which refer to AI-generated media content, have become a significant concern in recent times due to their potential use as a means for disinformation. Detecting DeepFakes has primarily relied on programmed machine learning algorithms. However, in this work, the researchers set out to explore the capabilities of multimodal large language models (LLMs) in DeepFake detection.
When it comes to media forensic tasks, multimodal LLMs are not inherently designed or tailored for such specific purposes. Despite this, the researchers conducted qualitative and quantitative experiments to demonstrate that multimodal LLMs can indeed expose AI-generated images. This is an exciting development as it opens up possibilities for detecting DeepFakes without the need for programming.
One of the strengths of multimodal LLMs lies in their ability to process multiple types of data, such as text and images. By leveraging the power of these models, the researchers were able to carefully design experiments and engineer prompts that could effectively identify AI-generated images. This multi-disciplinary approach combines language understanding and image analysis, highlighting the diverse nature of the concepts involved in DeepFake detection.
However, it is crucial to consider the limitations of multimodal LLMs in these tasks. While they have shown promise, there are still challenges to overcome. For instance, the researchers discuss the need for more extensive datasets that accurately represent the wide range of potential DeepFakes. The current limitations and biases of the available datasets can hinder the performance of these models and limit their real-world applicability.
Furthermore, multimodal LLMs may not be able to detect DeepFakes that have been generated using advanced techniques or by sophisticated adversaries who specifically aim to deceive these models. Adversarial attacks on AI models have been a topic of concern in various domains, and DeepFake detection is no exception. To improve the robustness of multimodal LLMs, researchers should explore adversarial training methods and continuously update the models to stay one step ahead of potential threats.
In conclusion, this work highlights the potential of multimodal large language models in DeepFake detection. By combining the strengths of language understanding and image analysis, these models can expose AI-generated media without the need for programming. However, further research and development are necessary to address the limitations, biases, and potential adversarial attacks. As the field of DeepFake detection continues to evolve, interdisciplinary collaboration and ongoing improvements in multimodal LLMs will play a pivotal role in combating disinformation and safeguarding the authenticity of media content.
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by jsendak | Feb 20, 2024 | Computer Science
arXiv:2402.10805v1 Announce Type: new
Abstract: The recent advancements in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effectively. Building upon this capability, we propose to enable multimodal large language models (MLLMs) to memorize and recall images within their parameters. Given a user query for visual content, the MLLM is anticipated to “recall” the relevant image from its parameters as the response. Achieving this target presents notable challenges, including inbuilt visual memory and visual recall schemes within MLLMs. To address these challenges, we introduce a generative cross-modal retrieval framework, which assigns unique identifier strings to represent images and involves two training steps: learning to memorize and learning to retrieve. The first step focuses on training the MLLM to memorize the association between images and their respective identifiers. The latter step teaches the MLLM to generate the corresponding identifier of the target image, given the textual query input. By memorizing images in MLLMs, we introduce a new paradigm to cross-modal retrieval, distinct from previous discriminative approaches. The experiments demonstrate that the generative paradigm performs effectively and efficiently even with large-scale image candidate sets.
Advancements in Generative Language Models and Cross-Modal Retrieval
In the field of natural language processing, generative language models have recently gained significant attention for their ability to generate coherent and contextually relevant text based on a given prompt. These models, such as GPT-3, have shown remarkable performance in tasks like text completion, translation, and question-answering. Building upon this capability, the authors of this paper propose extending the functionality of these models to incorporate visual content.
Traditionally, cross-modal retrieval refers to the task of retrieving relevant information from one modality (e.g., text) given a query from another modality (e.g., image). This has been primarily approached through discriminative models that try to learn a mapping between the two modalities and retrieve similar instances. However, the authors introduce a novel paradigm by proposing to “memorize” images within the parameters of the multimodal language model.
The key idea behind the proposed framework is to assign unique identifier strings to represent images and train the multimodal language model (MLLM) to memorize the association between these identifiers and the corresponding images. This involves two training steps: learning to memorize and learning to retrieve. During the first step, the MLLM learns to establish the connection between images and their identifiers. In the second step, it learns to generate the identifier of a target image given a textual query input.
The Challenges and Contributions
The main challenge in achieving this goal lies in developing visual memory and recall schemes within MLLMs. Unlike text, which can be easily tokenized and processed by language models, images are high-dimensional data that cannot be directly represented in a language model’s parameters. The authors propose an approach where images are encoded into their unique identifiers using techniques such as deep neural networks.
This proposed framework has several important implications and contributions. Firstly, it introduces a new perspective on cross-modal retrieval by leveraging the generative capabilities of MLLMs. This can potentially lead to more flexible and creative retrieval systems that go beyond simple similarity-based search. Secondly, it expands the scope of multimodal information processing by incorporating images into language models, which have traditionally focused on textual data. This approach allows for a more comprehensive understanding of the content and enables richer interactions between users and models.
Connections to Multimedia Information Systems and AR/VR
The presented research has strong connections to the wider field of multimedia information systems. Multimedia information systems deal with the storage, retrieval, and processing of various types of media, including text, images, audio, and video. The proposed framework addresses the challenge of integrating images seamlessly into language models, which are a fundamental component of multimedia information systems.
Furthermore, this research has implications for the domains of animations, artificial reality, augmented reality, and virtual realities. By enabling language models to memorize and recall images, the framework opens up possibilities for more immersive and interactive experiences in these domains. For example, virtual reality applications could leverage this capability to generate lifelike environments based on textual prompts, creating a more dynamic and realistic user experience.
Conclusion
The introduction of multimodal large language models (MLLMs) that can memorize and recall images presents exciting opportunities for cross-modal retrieval and extending the capabilities of language models. By leveraging generative approaches and training MLLMs to establish associations between images and unique identifiers, the proposed framework provides a new perspective on information retrieval. It also highlights the interdisciplinary nature of the concepts involved, connecting the fields of natural language processing, multimedia information systems, and virtual realities. As further research is conducted in this area, we can expect advancements in multimodal information processing and more immersive user experiences in various multimedia domains.
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