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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by jsendak | Jan 9, 2024 | Computer Science
Multimodal Large Language Models (MLLMs) are experiencing rapid growth,
yielding a plethora of noteworthy contributions in recent months. The
prevailing trend involves adopting data-driven methodologies, wherein diverse
instruction-following datasets are collected. However, a prevailing challenge
persists in these approaches, specifically in relation to the limited visual
perception ability, as CLIP-like encoders employed for extracting visual
information from inputs. Though these encoders are pre-trained on billions of
image-text pairs, they still grapple with the information loss dilemma, given
that textual captions only partially capture the contents depicted in images.
To address this limitation, this paper proposes to improve the visual
perception ability of MLLMs through a mixture-of-experts knowledge enhancement
mechanism. Specifically, we introduce a novel method that incorporates
multi-task encoders and visual tools into the existing MLLMs training and
inference pipeline, aiming to provide a more comprehensive and accurate
summarization of visual inputs. Extensive experiments have evaluated its
effectiveness of advancing MLLMs, showcasing improved visual perception
achieved through the integration of visual experts.
Multimodal Large Language Models (MLLMs) have been gaining momentum in recent months, thanks to their ability to generate meaningful content by leveraging both text and visual inputs. However, a significant challenge that researchers face when working with MLLMs is the limited visual perception ability of these models.
The existing approach involves using CLIP-like encoders to extract visual information from inputs. These encoders are pre-trained on billions of image-text pairs but still struggle with information loss due to the partial capture of contents in textual captions.
To overcome this limitation, this paper proposes a novel method that enhances the visual perception ability of MLLMs by incorporating a mixture-of-experts knowledge enhancement mechanism. This approach integrates multi-task encoders and visual tools into the training and inference pipeline of MLLMs, enabling a more comprehensive and accurate summarization of visual inputs.
The significance of this research lies in its multi-disciplinary nature. It combines elements from various domains such as natural language processing, computer vision, and artificial intelligence. By leveraging the strengths of different disciplines, the proposed method aims to improve the overall performance of MLLMs when it comes to understanding and generating content based on visual inputs.
In the wider field of multimedia information systems, this research contributes to bridging the gap between textual and visual information processing. With the integration of visual experts into MLLMs, the models become more adept at understanding and leveraging visual cues, leading to enhanced performance in tasks such as image captioning, visual question answering, and content generation.
Additioally, this work has implications for the advancements in Animations, Artificial Reality, Augmented Reality, and Virtual Realities. With better visual perception ability, MLLMs can play a crucial role in generating realistic animations, improving the user experience in artificial and augmented reality applications, and enabling more immersive virtual reality environments. By training MLLMs to understand and interpret visual inputs effectively, these technologies can benefit from more accurate and context-aware content generation.
In conclusion, the proposed method for enhancing the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism presents a promising avenue for advancing these models. By incorporating multi-task encoders and visual tools, the proposed approach enables MLLMs to have a more comprehensive understanding of visual inputs, thereby improving their performance across various domains including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 2, 2024 | Computer Science
The rapid evolution of Multi-modality Large Language Models (MLLMs) has
catalyzed a shift in computer vision from specialized models to general-purpose
foundation models. Nevertheless, there is still an inadequacy in assessing the
abilities of MLLMs on low-level visual perception and understanding. To address
this gap, we present Q-Bench, a holistic benchmark crafted to systematically
evaluate potential abilities of MLLMs on three realms: low-level visual
perception, low-level visual description, and overall visual quality
assessment. a) To evaluate the low-level perception ability, we construct the
LLVisionQA dataset, consisting of 2,990 diverse-sourced images, each equipped
with a human-asked question focusing on its low-level attributes. We then
measure the correctness of MLLMs on answering these questions. b) To examine
the description ability of MLLMs on low-level information, we propose the
LLDescribe dataset consisting of long expert-labelled golden low-level text
descriptions on 499 images, and a GPT-involved comparison pipeline between
outputs of MLLMs and the golden descriptions. c) Besides these two tasks, we
further measure their visual quality assessment ability to align with human
opinion scores. Specifically, we design a softmax-based strategy that enables
MLLMs to predict quantifiable quality scores, and evaluate them on various
existing image quality assessment (IQA) datasets. Our evaluation across the
three abilities confirms that MLLMs possess preliminary low-level visual
skills. However, these skills are still unstable and relatively imprecise,
indicating the need for specific enhancements on MLLMs towards these abilities.
We hope that our benchmark can encourage the research community to delve deeper
to discover and enhance these untapped potentials of MLLMs. Project Page:
https://q-future.github.io/Q-Bench.
Multimodal Large Language Models: Assessing Low-Level Visual Skills
The field of computer vision has experienced a shift from specialized models to more general-purpose foundation models, thanks to the rapid evolution of Multi-modality Large Language Models (MLLMs). These models have shown great potential in various tasks but are still lacking in their ability to perceive and understand low-level visual information. To address this gap, a team of researchers presents Q-Bench, a benchmark designed to systematically evaluate the potential abilities of MLLMs.
Assessing Low-Level Visual Perception
To evaluate the low-level perception ability of MLLMs, the researchers have constructed the LLVisionQA dataset. This dataset consists of 2,990 images from diverse sources, each accompanied by a human-asked question focusing on its low-level attributes. The MLLMs are then evaluated based on their correctness in answering these questions. This task provides insights into how well MLLMs understand and perceive low-level visual characteristics.
Evaluating Low-Level Visual Description
In addition to perception, the researchers also assess the description ability of MLLMs on low-level information. The LLDescribe dataset is introduced, which contains expert-labelled golden low-level text descriptions for 499 images. A GPT-involved comparison pipeline is employed to compare the outputs of MLLMs with these expert descriptions. This evaluation allows for an examination of how effectively MLLMs generate accurate descriptions based on low-level visual information.
Measuring Visual Quality Assessment
Besides perception and description, the Q-Bench benchmark includes measuring the visual quality assessment ability of MLLMs. A softmax-based strategy is designed to enable MLLMs to predict quantifiable quality scores. The researchers evaluate the MLLMs on various existing image quality assessment (IQA) datasets, aligning their predictions with human opinion scores. This assessment provides insights into how well MLLMs can judge and assess the visual quality of images.
The evaluation across these three abilities confirms that MLLMs possess preliminary low-level visual skills. However, it also reveals that these skills are still relatively unstable and imprecise, indicating the need for specific enhancements. The multi-disciplinary nature of the benchmark highlights the intersection of computer vision, natural language processing, and artificial intelligence.
The findings and insights gained from Q-Bench open up avenues for future research and enhancements in MLLMs. The benchmark serves as a call to action for the research community to delve deeper into uncovering and improving the untapped potential of MLLMs in perceiving, describing, and assessing low-level visual information. By focusing on these important aspects, we can push the boundaries of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, leading to more advanced and effective applications in various domains.
More information about the Q-Bench benchmark can be found on the project page: https://q-future.github.io/Q-Bench.
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by jsendak | Jan 1, 2024 | Computer Science
In this paper, we focus on editing Multimodal Large Language Models (MLLMs).
Compared to editing single-modal LLMs, multimodal model editing is more
challenging, which demands a higher level of scrutiny and careful consideration
in the editing process. To facilitate research in this area, we construct a new
benchmark, dubbed MMEdit, for editing multimodal LLMs and establishing a suite
of innovative metrics for evaluation. We conduct comprehensive experiments
involving various model editing baselines and analyze the impact of editing
different components for multimodal LLMs. Empirically, we notice that previous
baselines can implement editing multimodal LLMs to some extent, but the effect
is still barely satisfactory, indicating the potential difficulty of this task.
We hope that our work can provide the NLP community with insights. Code and
dataset are available in https://github.com/zjunlp/EasyEdit.
Multimodal Large Language Models (MLLMs) and the Challenges of Editing
In recent years, Multimodal Large Language Models (MLLMs) have garnered significant attention in the field of multimedia information systems. These models, which integrate multiple modalities such as text, images, and even audio, have shown great promise in various applications, including text generation, image captioning, and visual question answering. However, one of the critical challenges associated with MLLMs is editing.
The process of editing multimodal models is far more complex compared to single-modal models. It demands a higher level of scrutiny and careful consideration. This complexity arises due to the need to ensure coherence across different modalities while preserving semantic meaning and maintaining the desired style. For instance, if we want to edit a text generated by an MLLM to change the image content it describes, we must ensure that the modified text remains coherent and aligns with the new image.
Introducing MMEdit: A Benchmark for Editing Multimodal LLMs
To facilitate research in the area of editing multimodal LLMs, the authors of this paper have constructed a new benchmark called MMEdit. This benchmark provides a standardized evaluation framework for testing the effectiveness of various editing techniques and algorithms. By establishing this benchmark, researchers can objectively compare different approaches and measure their performance.
Furthermore, the authors have also introduced a suite of innovative metrics specifically tailored to evaluate the quality of edited multimodal LLMs. These metrics take into account various factors including semantic coherence, style preservation, and alignment between different modalities. This comprehensive evaluation framework will enable researchers to gain deeper insights into the strengths and limitations of different editing techniques.
The Impact of Editing Different Components and Baselines
To analyze the impact of editing different components of multimodal LLMs, the authors conduct comprehensive experiments. They compare the performance of various editing baselines and measure their effectiveness in achieving the desired edits. The results indicate that while previous baselines can achieve some level of editing in multimodal models, the overall effect is still unsatisfactory.
This finding highlights the potential difficulty of the task at hand. It emphasizes the need for further research and development to improve the quality of edited multimodal LLMs. The findings also suggest that existing editing techniques may need to be enhanced or new approaches need to be devised to address the unique challenges posed by these models.
The Wider Field of Multimedia Information Systems and its Connection to AR, VR, and Animation
This paper on editing multimodal LLMs has significant implications for the wider field of multimedia information systems. As we continue to develop advanced technologies such as Augmented Reality (AR), Virtual Reality (VR), and animations, the integration of different modalities, including text and images, becomes crucial. The ability to edit multimodal LLMs effectively can enhance the quality and realism of AR and VR experiences, improve interactive animations, and enable more immersive storytelling.
By focusing on the challenges and techniques associated with editing multimodal LLMs, this research contributes to the advancement of AR, VR, and animation technologies. It lays the groundwork for developing more sophisticated tools and algorithms that can seamlessly edit multimodal content in these domains. This multidisciplinary nature of the research highlights the intersection between natural language processing, multimedia information systems, AR, VR, and animation, emphasizing the need for collaboration between experts from different fields.
In conclusion, the construction of the MMEdit benchmark, the analysis of editing baselines, and the identification of the challenges in editing multimodal LLMs provide significant insights for the NLP community and the wider field of multimedia information systems. This work sets the stage for future research endeavors to tackle the complexity of editing multimodal models and drive innovations in AR, VR, and animation.
Code and dataset for this research can be found at https://github.com/zjunlp/EasyEdit.
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by jsendak | Dec 31, 2023 | Computer Science
The article introduces a Cloud-Device Collaborative Continual Adaptation framework to enhance the performance of compressed, device-deployed Multimodal Large Language Models (MLLMs). This framework addresses the challenge of deploying large-scale MLLMs on client devices, which often results in a decline in generalization capabilities when the models are compressed.
The framework consists of three key components:
1. Device-to-Cloud Uplink:
In the uplink phase, the Uncertainty-guided Token Sampling (UTS) strategy is employed to filter out-of-distribution tokens. This helps reduce transmission costs and improve training efficiency by focusing on relevant information for cloud-based adaptation.
2. Cloud-Based Knowledge Adaptation:
The proposed Adapter-based Knowledge Distillation (AKD) method enables the transfer of refined knowledge from larger-scale MLLMs in the cloud to compressed, pocket-size MLLMs on the device. This allows the device models to benefit from the robust capabilities of the larger-scale models without requiring extensive computational resources.
3. Cloud-to-Device Downlink:
In the downlink phase, the Dynamic Weight update Compression (DWC) strategy is introduced. This strategy adaptively selects and quantizes updated weight parameters, enhancing transmission efficiency and reducing the representational disparity between the cloud and device models. This ensures that the models remain consistent and synchronized during deployment.
The article highlights that extensive experiments on multimodal benchmarks demonstrate the superiority of the proposed framework compared to prior Knowledge Distillation and device-cloud collaboration methods. It is worth noting that the feasibility of the approach has also been validated through real-world experiments.
This research has significant implications for the deployment of large-scale MLLMs on client devices. By leveraging cloud-based resources and employing strategies for efficient data transmission, knowledge adaptation, and weight parameter compression, the proposed framework enables compressed MLLMs to maintain their performance and generalization capabilities. This can greatly enhance the usability and effectiveness of MLLMs in various applications where device resources are limited.
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