by jsendak | Nov 4, 2024 | Computer Science
arXiv:2411.00304v1 Announce Type: cross
Abstract: In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle various complex tasks, yet issues such as hallucinations and weak object discrimination persist. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation. This paper addresses these challenges by proposing a unified approach that integrates the strengths of both paradigms. Considering interleaved image-text sequences as the general format of input samples, we introduce a structure-induced training strategy that imposes semantic relationships between input samples and the MLLM’s hidden state. This approach enhances the MLLM’s ability to capture global semantics and distinguish fine-grained semantics. By leveraging dynamic sequence alignment within the Dynamic Time Warping framework and integrating a novel kernel for fine-grained semantic differentiation, our method effectively balances generative and discriminative tasks. Extensive experiments demonstrate the effectiveness of our approach, achieving state-of-the-art results in multiple generative tasks, especially those requiring cognitive and discrimination abilities. Additionally, our method surpasses discriminative benchmarks in interleaved and fine-grained retrieval tasks. By employing a retrieval-augmented generation strategy, our approach further enhances performance in some generative tasks within one model, offering a promising direction for future research in vision-language modeling.
Integration of Generative and Discriminative Approaches in Vision-Language Models
Over the past few years, Vision-Language Models (VLMs) have made significant progress in understanding and generating text based on visual input. However, two predominant paradigms have emerged in training these models, each with its own limitations. Generative training has allowed Multimodal Large Language Models (MLLMs) to tackle various complex tasks, but issues like hallucinations and weak object discrimination still persist. On the other hand, discriminative training, exemplified by models like CLIP, performs well in zero-shot image-text classification and retrieval but struggles with more complex scenarios that require fine-grained semantic differentiation.
This paper proposes a unified approach that integrates the strengths of both paradigms to tackle these challenges. The authors consider interleaved image-text sequences as the general format of input samples and introduce a structure-induced training strategy that imposes semantic relationships between these input samples and the MLLM’s hidden state. By doing so, they enhance the model’s ability to capture global semantics and distinguish fine-grained semantics.
One interesting aspect of this approach is the use of dynamic sequence alignment within the Dynamic Time Warping framework. This helps align the image and text sequences, allowing for better understanding of the relationships between them. Additionally, the authors propose a novel kernel for fine-grained semantic differentiation, further enhancing the model’s discriminative abilities.
The multi-disciplinary nature of this work is evident in its connections to various fields. In the wider field of multimedia information systems, this work contributes by providing a more effective way of combining visual and textual information. By addressing the limitations of generative and discriminative models, the proposed approach opens up new possibilities for applications in animations, artificial reality, augmented reality, and virtual realities.
For example, in animations, this approach could improve the generation of text captions or dialogue based on visual scenes. It could also enhance the understanding of complex scenarios in virtual reality environments, allowing for more immersive experiences. Furthermore, in augmented reality applications, the integration of generative and discriminative approaches could enable better object recognition and understanding of the surrounding environment.
The experiments conducted by the authors demonstrate the effectiveness of their approach, achieving state-of-the-art results in multiple generative tasks, particularly those requiring cognitive and discrimination abilities. Additionally, their method surpasses discriminative benchmarks in interleaved and fine-grained retrieval tasks.
By employing a retrieval-augmented generation strategy, the authors further enhance the performance of generative tasks within one model, offering a promising direction for future research in vision-language modeling. This integration of retrieval and generation could lead to breakthroughs in areas such as interactive storytelling, where the model can generate text based on retrieved information from a large knowledge base.
In conclusion, the unified approach proposed in this paper addresses the challenges of generative and discriminative training in Vision-Language Models by integrating the strengths of both paradigms. The multi-disciplinary nature of this work allows it to have implications in the broader field of multimedia information systems and its related domains, such as animations, artificial reality, augmented reality, and virtual realities. The experiments presented demonstrate the effectiveness of the proposed approach, and the retrieval-augmented generation strategy opens up exciting possibilities for future research in vision-language modeling.
Read the original article
by jsendak | Oct 22, 2024 | Computer Science
arXiv:2410.14154v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. MLLMs involve significant external knowledge within their parameters; however, it is challenging to continually update these models with the latest knowledge, which involves huge computational costs and poor interpretability. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs. Considering the redundant information within vision modality, we first leverage the question to instruct the extraction of visual information through interactions with one set of learnable queries, minimizing irrelevant interference during retrieval and generation. Besides, we introduce a pre-trained multimodal adaptive fusion module to achieve question text-to-multimodal retrieval and integration of multimodal knowledge by projecting visual and language modalities into a unified semantic space. Furthermore, we present an Adaptive Selection Knowledge Generation (ASKG) strategy to train the generator to autonomously discern the relevance of retrieved knowledge, which realizes excellent denoising performance. Extensive experiments on open multimodal question-answering datasets demonstrate that RA-BLIP achieves significant performance and surpasses the state-of-the-art retrieval-augmented models.
Expert Commentary: The Future of Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have been gaining considerable attention in recent years, and their potential as versatile models for vision-language tasks is becoming increasingly evident. However, one of the major challenges with these models is the constant update of external knowledge, as it involves significant computational costs and lacks interpretability. This is where retrieval augmentation techniques come into play, offering effective solutions for enhancing both LLMs and MLLMs.
In this study, a novel framework called multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP) is proposed. The framework takes advantage of the question to guide the extraction of visual information, minimizing irrelevant interference and allowing for more accurate retrieval and generation. Additionally, a pre-trained multimodal adaptive fusion module is introduced to achieve text-to-multimodal retrieval and integration of knowledge across different modalities.
One of the key features of the proposed framework is the Adaptive Selection Knowledge Generation (ASKG) strategy, which enables the generator to autonomously discern the relevance of retrieved knowledge. This strategy ensures excellent denoising performance and enhances the overall effectiveness of the model.
The results of extensive experiments conducted on multimodal question-answering datasets show that RA-BLIP outperforms existing retrieval-augmented models, demonstrating its potential as a state-of-the-art solution in the field.
Multi-disciplinary Nature and Relation to Multimedia Information Systems and AR/VR
The concepts explored in this study are highly multi-disciplinary and have strong connections to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
By combining language and vision modalities, multimodal large language models bridge the gap between textual and visual information, enabling more effective communication and understanding. This has direct implications for multimedia information systems, where the integration of various media types (such as text, images, videos, etc.) is crucial for efficient information retrieval and processing.
Furthermore, the use of retrieval augmentation techniques, as demonstrated in RA-BLIP, can significantly enhance the performance of multimedia information systems. By incorporating external knowledge and allowing for dynamic updates, these techniques enable better retrieval of relevant information and improve the overall user experience.
In the context of artificial reality, augmented reality, and virtual realities, multimodal large language models play a vital role in bridging the gap between virtual and real worlds. By understanding and generating both textual and visual content, these models can enable more immersive and interactive experiences in these virtual environments. This has implications for various applications, such as virtual reality gaming, education, and training simulations.
Overall, the findings of this study highlight the potential of multimodal large language models and retrieval augmentation techniques in advancing the field of multimedia information systems, as well as their relevance to the broader domains of artificial reality, augmented reality, and virtual realities.
Read the original article
by jsendak | Sep 30, 2024 | AI
arXiv:2409.18142v1 Announce Type: new
Abstract: The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have largely concentrated on model architectures and training methodologies, a thorough analysis of the benchmarks used for evaluating these models remains underexplored. This survey addresses this gap by systematically reviewing 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application. We provide a detailed analysis of task designs, evaluation metrics, and dataset constructions, across diverse modalities. We hope that this survey will contribute to the ongoing advancement of MLLM research by offering a comprehensive overview of benchmarking practices and identifying promising directions for future work. An associated GitHub repository collecting the latest papers is available.
The Significance of Multimodal Large Language Models (MLLMs)
Over the years, Multimodal Large Language Models (MLLMs) have witnessed rapid evolution, revolutionizing the field of artificial intelligence. These models have significantly enhanced our capability to understand and generate multimodal content, which has numerous practical applications across various industries. However, while researchers have focused primarily on model architectures and training methodologies, the benchmarks used to evaluate these models have received limited attention.
This survey aims to bridge this gap by systematically reviewing 211 benchmarks that assess MLLMs across four fundamental domains: understanding, reasoning, generation, and application. By diving deep into the task designs, evaluation metrics, and dataset constructions, the survey sheds light on the intricacies of evaluating MLLMs across diverse modalities.
The Multi-Disciplinary Nature of MLLM Research
One of the key takeaways from this survey is the multi-disciplinary nature of MLLM research. Due to the complex nature of multimodal content, effectively evaluating MLLMs requires expertise from various fields. Linguists, computer scientists, psychologists, and domain experts from different industries must collaborate to construct meaningful benchmarks that capture the richness and complexity of multimodal data.
These benchmarks are not limited to a single modality; instead, they encompass a wide range of input types, including text, images, videos, and audio. The diverse nature of the benchmarks ensures that MLLMs are tested against real-world scenarios, where multimodal content is inherently entangled, requiring the models to understand and generate content in a coherent and meaningful manner.
Identifying Promising Directions for Future Work
By analyzing the current benchmarking practices, this survey also identifies several promising directions for future MLLM research. One notable area is the development of more comprehensive and challenging benchmarks that can better evaluate MLLMs’ capabilities. These benchmarks should strive to capture the nuances and context-dependent nature of multimodal content, providing opportunities for innovative research and development of MLLMs.
In addition, the survey emphasizes the importance of standardized evaluation metrics and guidelines for benchmarking MLLMs. This standardization would enable fair comparisons between different models and facilitate progress in the field. Researchers should work towards consensus on evaluation metrics, considering factors such as objectivity, interpretability, and alignment with human judgment.
The associated GitHub repository, which collects the latest papers in the field, serves as a valuable resource for researchers and practitioners seeking to stay updated on the advancements in MLLM research.
Conclusion
This survey provides a comprehensive overview of benchmarking practices for Multimodal Large Language Models (MLLMs). It highlights the multi-disciplinary nature of MLLM research, which requires collaboration between experts from various fields. The survey also identifies promising directions for future work, emphasizing the need for more challenging benchmarks and standardized evaluation metrics. By addressing these considerations, researchers can further advance the capabilities of MLLMs and unlock their potential in understanding and generating multimodal content.
Keywords: Multimodal Large Language Models, MLLMs, benchmarking practices, evaluation metrics, multimodal content.
Read the original article
by jsendak | Sep 21, 2024 | Computer Science
arXiv:2409.12319v1 Announce Type: cross
Abstract: Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. For example, in the audio and speech domains, an LLM can be equipped with (automatic) speech recognition (ASR) abilities by just concatenating the audio tokens, computed with an audio encoder, and the text tokens to achieve state-of-the-art results. On the contrary, tasks like visual and audio-visual speech recognition (VSR/AVSR), which also exploit noise-invariant lip movement information, have received little or no attention. To bridge this gap, we propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities. It leverages pre-trained audio and video encoders to produce modality-specific tokens which, together with the text tokens, are processed by a pre-trained LLM (e.g., Llama3.1-8B) to yield the resulting response in an auto-regressive fashion. Llama-AVSR requires a small number of trainable parameters as only modality-specific projectors and LoRA modules are trained whereas the multi-modal encoders and LLM are kept frozen. We evaluate our proposed approach on LRS3, the largest public AVSR benchmark, and we achieve new state-of-the-art results for the tasks of ASR and AVSR with a WER of 0.81% and 0.77%, respectively. To bolster our results, we investigate the key factors that underpin the effectiveness of Llama-AVSR: the choice of the pre-trained encoders and LLM, the efficient integration of LoRA modules, and the optimal performance-efficiency trade-off obtained via modality-aware compression rates.
Analysis: Multimodal Large Language Models in Multimedia Information Systems
The concept of multimodal large language models (MLLMs) is a cutting-edge area of research that combines artificial intelligence, natural language processing, and computer vision to enhance the understanding of textual, audio, and visual information. MLLMs have gained significant attention due to their impressive capabilities in analyzing multimodal data and performing various tasks such as speech recognition, image captioning, and video understanding.
In this particular study, the focus is on the audio and visual domains, specifically audio-visual speech recognition (AVSR). While ASR (automatic speech recognition) has advanced significantly with the help of MLLMs, AVSR has received relatively little attention. AVSR is a challenging task as it requires understanding not only the audio but also the visual signals, particularly lip movement.
The proposed model, Llama-AVSR, aims to bridge this gap in AVSR research. It leverages pre-trained audio and video encoders to extract modality-specific tokens, which are then combined with text tokens and processed through a pre-trained LLM. By adopting an auto-regressive approach, Llama-AVSR generates highly accurate responses for both ASR and AVSR tasks.
One key aspect of Llama-AVSR is the use of frozen multi-modal encoders and LLM, which means that they are not fine-tuned during training. Instead, only modality-specific projectors and LoRA (Local Refinement Attention) modules are trained. This approach allows for efficient training and parameter optimization while still achieving state-of-the-art results.
The authors evaluate Llama-AVSR using the LRS3 benchmark, which is a widely-used dataset for AVSR. The results demonstrate the effectiveness of the proposed approach, achieving a Word Error Rate (WER) of 0.81% for ASR and 0.77% for AVSR, which are new state-of-the-art performances.
This study showcases the interdisciplinary nature of multimedia information systems, combining elements from audio processing, computer vision, and natural language understanding. By integrating audio and visual information, MLLMs like Llama-AVSR have the potential to revolutionize various applications such as speech recognition systems, virtual reality experiences, and interactive multimedia content.
Implications for Animations, Artificial Reality, Augmented Reality, and Virtual Realities
Animations, artificial reality, augmented reality, and virtual realities greatly benefit from the advancements in multimodal large language models such as Llama-AVSR. The ability to understand and process both audio and visual information opens up new possibilities for creating immersive and realistic experiences in these domains.
In the context of animations, MLLMs can enhance the creation and synchronization of animated characters’ lip movements with the corresponding dialogue or speech. By utilizing the lip movement information, as considered in AVSR, animations can be more accurate and lifelike. This can significantly improve the quality of animated movies, TV shows, and video games, bringing characters to life in a way that closely matches the intended audio.
For artificial reality, MLLMs can play a crucial role in bridging the gap between artificial intelligence and virtual environments. By understanding and responding to multimodal inputs from users, AI-powered virtual agents or characters can engage in more realistic and natural interactions. This can enhance the overall user experience, making artificial reality environments feel more immersive and interactive.
In augmented reality applications, MLLMs like Llama-AVSR can contribute to more accurate speech recognition and understanding. For example, in AR systems that involve voice commands or speech-based interactions, having a robust AVSR capability can improve the accuracy of speech recognition, enabling more intuitive and reliable interactions between users and augmented environments.
Virtual reality experiences can also benefit from the advancements in MLLMs. By analyzing both audio and visual cues, virtual reality systems can provide more realistic and context-aware simulations. For instance, within a virtual reality game, the recognition of audio-visual speech can be used to enhance the understanding of the player’s voice commands and facilitate more intelligent and immersive gameplay.
In conclusion, the development of multimodal large language models, exemplified by Llama-AVSR, has far-reaching implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By combining expertise from multiple disciplines, these models open up exciting possibilities for advanced multimodal processing and more immersive and realistic experiences in various domains.
Read the original article
by jsendak | Sep 18, 2024 | Computer Science
Expert Commentary: Generating Geometric Images for Mathematical Reasoning
Large language models have shown great promise in various NLP tasks, but their capabilities in mathematical reasoning have only recently started to be explored. Previous research has mainly focused on text-based algebra problems, leaving a gap in the study of geometry. One of the main challenges in advancing research in this area has been the lack of high-quality geometric datasets.
This paper introduces AutoGeo, a novel approach for automatically generating mathematical geometric images. By leveraging precisely defined geometric clauses, AutoGeo is able to create a diverse range of geometry image-text pairs. This includes various geometric shapes such as lines, polygons, circles, and complex spatial relationships.
The creation of AutoGeo-100k, an extensive repository comprising 100k high-quality geometry image-text pairs, is a significant contribution of this work. This dataset will not only fill the critical gap in the availability of geometric datasets but also fuel further research and development of sophisticated AI-driven tools in education and research.
One of the key applications of AutoGeo-100k is enhancing the performance of multimodal large language models through fine-tuning. Experimental results have shown that these models trained on AutoGeo-100k exhibit improved accuracy in tasks like geometric captioning and mathematical reasoning. This indicates the effectiveness of AutoGeo-100k in enhancing the model’s ability to handle geometric images.
The implications of this research are far-reaching. The availability of AutoGeo-100k will not only enable the development of AI models that can understand and reason about geometric problems but also help in the development of AI-driven educational tools. Such tools can provide personalized feedback and assistance to students studying geometry, making the learning process more interactive and engaging.
Furthermore, this work opens up new possibilities for research in the intersection of AI and geometry. Researchers can now explore how large language models can be utilized to solve complex geometric problems, paving the way for more sophisticated AI algorithms in the field.
In conclusion, the introduction of AutoGeo and the creation of AutoGeo-100k dataset address the lack of high-quality geometric datasets and significantly contribute to the advancement of AI-driven tools in education and research. This research serves as a milestone in the exploration of large language models’ capabilities in mathematical reasoning and opens up exciting avenues for future research in the field.
Project page: https://this_url.
Read the original article
