by jsendak | Mar 19, 2024 | Computer Science
arXiv:2403.10943v1 Announce Type: new
Abstract: Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. We introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes. Besides 9,304 in-scope samples, it also includes 5,736 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world scenarios. Furthermore, we provide comprehensive information on the speakers in each utterance, enriching its utility for multi-party conversational research. We establish a general framework supporting the organization of single-turn and multi-turn dialogue data, modality feature extraction, multimodal fusion, as well as in-scope classification and out-of-scope detection. Evaluation benchmarks are built using classic multimodal fusion methods, ChatGPT, and human evaluators. While existing methods incorporating nonverbal information yield improvements, effectively leveraging context information and detecting out-of-scope samples remains a substantial challenge. Notably, large language models exhibit a significant performance gap compared to humans, highlighting the limitations of machine learning methods in the cognitive intent understanding task. We believe that MIntRec2.0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications. The full dataset and codes are available at https://github.com/thuiar/MIntRec2.0.
Introduction
Multimodal intent recognition is a complex task that involves understanding human intentions through the incorporation of non-verbal modalities from real-world contexts. In order to enhance the comprehension of human intentions, it is crucial to have access to large-scale benchmark datasets that accurately capture the intricacies of multi-party conversational interactions. However, existing datasets in this field suffer from limitations in scale and difficulties in handling out-of-scope samples.
MIntRec2.0: A Comprehensive Benchmark Dataset
The MIntRec2.0 dataset aims to address these limitations by providing a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. The dataset consists of 1,245 dialogues with a total of 15,040 samples. Each sample is annotated within a new intent taxonomy comprising 30 fine-grained classes. Notably, the dataset includes both in-scope samples (9,304) and out-of-scope samples (5,736) that naturally occur in multi-turn contexts.
The Importance of Multi-disciplinarity
The nature of multimodal intent recognition highlights the interdisciplinary nature of this field. It requires expertise in areas such as natural language processing, computer vision, machine learning, and cognitive science. By incorporating non-verbal modalities and contextual information, researchers are able to develop more accurate and comprehensive models for understanding human intentions in conversational interactions.
Related to Multimedia Information Systems
Multimedia information systems play a crucial role in multimodal intent recognition. The integration of various modalities, including text, images, and audio, enables a more comprehensive understanding of human intentions. The MIntRec2.0 dataset provides a valuable resource for exploring new techniques and algorithms in the field of multimedia information systems, and offers opportunities for advancements in areas such as multimodal fusion, feature extraction, and classification.
Animations, Artificial Reality, Augmented Reality, and Virtual Realities
In the context of animations, artificial reality, augmented reality, and virtual realities, multimodal intent recognition can greatly enhance user experiences. By understanding human intentions through multiple modalities, these technologies can tailor their responses and interactions to meet users’ needs and preferences. For example, in virtual reality environments, the ability to accurately recognize and interpret human intentions can enable more realistic and immersive experiences.
Evaluation and Future Directions
The MIntRec2.0 dataset provides a solid foundation for evaluating the performance of existing multimodal fusion methods, language models such as ChatGPT, and human evaluators in the field of multimodal intent recognition. However, it also highlights the challenges that remain, particularly in effectively leveraging context information and detecting out-of-scope samples. Notably, large language models still exhibit a significant performance gap compared to humans, emphasizing the limitations of current machine learning methods in cognitive intent understanding tasks.
In the future, research in this field could focus on developing more advanced multimodal fusion methods, improving context understanding, and addressing the challenges associated with out-of-scope detection. Additionally, efforts to bridge the performance gap between machine learning methods and human performance could lead to significant advancements in the field of multimodal intent recognition.
Conclusion
The MIntRec2.0 dataset serves as a valuable resource for researchers and practitioners working in the field of human-machine conversational interactions. By providing a large-scale benchmark dataset and comprehensive information on multi-party conversations, it lays the groundwork for advancements in multimodal intent recognition. The interdisciplinary nature of this field, along with its connections to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, further highlight its potential for transforming various domains and applications.
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by jsendak | Mar 19, 2024 | Computer Science
As an expert commentator, I find the research presented in this article on verifying question validity before answering to be highly relevant and valuable. In real-world applications, users often provide imperfect instructions or queries, which can lead to inaccurate or irrelevant answers. Therefore, it is essential to have a model that not only generates the best possible answer but also addresses the discrepancies in the query and communicates them to the users.
INTRODUCING VISREAS DATASET
The introduction of the VISREAS dataset is a significant contribution to the field of compositional visual question answering. This dataset comprises both answerable and unanswerable visual queries, created by manipulating commonalities and differences among objects, attributes, and relations. The use of Visual Genome scene graphs to generate 2.07 million semantically diverse queries ensures the dataset’s authenticity and wide range of query variations.
The Challenge of Question Answerability
The unique challenge in this task lies in validating the answerability of a question with respect to an image before providing an answer. This requirement reflects the real-world scenario where humans need to determine whether a question is relevant to the given context. State-of-the-art models have struggled to perform well on this task, highlighting the need for new approaches and benchmarks.
LOGIC2VISION: A New Modular Baseline
To address the limitations of existing models, the researchers propose LOGIC2VISION, a new modular baseline model. LOGIC2VISION takes a unique approach by reasoning through the production and execution of pseudocode, without relying on external modules for answer generation.
The use of pseudocode allows LOGIC2VISION to break down the problem into logical steps and explicitly represent the reasoning process. By generating and executing pseudocode, the model can better understand the question’s requirements and constraints, leading to more accurate answers.
Improved Performance and Significant Gain
The results presented in this article demonstrate the effectiveness of LOGIC2VISION in addressing the challenge of question answerability. LOGIC2VISION outperforms generative models in the VISREAS dataset, achieving an improvement of 4.82% over LLaVA-1.5 and 12.23% over InstructBLIP.
Furthermore, LOGIC2VISION also demonstrates a significant gain in performance compared to classification models. This finding suggests that the novel approach of reasoning through the production and execution of pseudocode is a promising direction for addressing question validity.
Future Directions
While LOGIC2VISION shows promising results, there are still opportunities for further improvement and exploration. Future research could focus on enhancing the pseudocode generation process and refining the execution mechanism to better handle complex queries and diverse visual contexts.
Additionally, expanding the evaluation of models on larger and more diverse datasets would provide a more comprehensive understanding of their performance. This could involve exploring the use of other scene graph datasets or even extending the VISREAS dataset with additional annotations and variations.
In conclusion, the introduction of the VISREAS dataset and the development of the LOGIC2VISION model represent significant advancements in addressing question answerability in visual question-answering tasks. This research tackles an important real-world problem and provides valuable insights and solutions. As the field continues to evolve, it will be exciting to see further advancements and refinements in this area.
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by jsendak | Mar 18, 2024 | Computer Science
arXiv:2403.10406v1 Announce Type: new
Abstract: There has emerged a growing interest in exploring efficient quality assessment algorithms for image super-resolution (SR). However, employing deep learning techniques, especially dual-branch algorithms, to automatically evaluate the visual quality of SR images remains challenging. Existing SR image quality assessment (IQA) metrics based on two-stream networks lack interactions between branches. To address this, we propose a novel full-reference IQA (FR-IQA) method for SR images. Specifically, producing SR images and evaluating how close the SR images are to the corresponding HR references are separate processes. Based on this consideration, we construct a deep Bi-directional Attention Network (BiAtten-Net) that dynamically deepens visual attention to distortions in both processes, which aligns well with the human visual system (HVS). Experiments on public SR quality databases demonstrate the superiority of our proposed BiAtten-Net over state-of-the-art quality assessment methods. In addition, the visualization results and ablation study show the effectiveness of bi-directional attention.
Analysis of Image Super-Resolution Quality Assessment
Image super-resolution (SR) is a technique used to enhance the resolution and details of low-resolution images. As the demand for high-quality images continues to grow, there is a need for efficient quality assessment algorithms for SR. This article focuses on the use of deep learning techniques, specifically dual-branch algorithms, to automatically evaluate the visual quality of SR images.
The concept of dual-branch algorithms is an interesting one, as it involves using two separate processes: producing SR images and evaluating their closeness to the corresponding high-resolution (HR) references. This approach recognizes the fact that the evaluation process and the SR generation process are distinct and should be treated as such.
To address the challenge of lack of interactions between the branches in existing SR image quality assessment (IQA) metrics, the authors propose a novel full-reference IQA method called BiAtten-Net. This deep Bi-directional Attention Network dynamically deepens visual attention to distortions in both processes, mimicking the human visual system (HVS).
This research has significant implications in the field of multimedia information systems, as it combines concepts from computer vision, deep learning, and image processing. The multi-disciplinary nature of this work highlights the need for collaboration across different domains.
Furthermore, this work is related to the wider field of animations, artificial reality, augmented reality, and virtual realities. SR techniques are often used in these fields to enhance the visual quality of images and videos. The ability to automatically assess the quality of SR images is crucial for ensuring optimal user experiences in these applications.
The experiments conducted in this study demonstrate the superiority of the proposed BiAtten-Net over existing quality assessment methods. The visualization results and ablation study provide additional evidence of the effectiveness of the bi-directional attention approach.
In conclusion, this article presents a novel approach to image super-resolution quality assessment using deep learning techniques and bi-directional attention. The findings of this research have implications not only in the field of image processing but also in the broader context of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 18, 2024 | Computer Science
The Problem of Image-to-Image Translation: Challenges and Potential Impact
The problem of image-to-image translation has become increasingly intriguing and challenging in recent years due to its potential impact on various computer vision applications such as colorization, inpainting, and segmentation. This problem involves extracting patterns from one domain and successfully applying them to another domain in an unsupervised (unpaired) manner. The complexity of this task has attracted significant attention and has led to the development of deep generative models, particularly Generative Adversarial Networks (GANs).
Unlike other theoretical applications of GANs, image-to-image translation has achieved real-world impact through impressive results. This success has propelled GANs into the spotlight in the field of computer vision. One seminal work in this area is CycleGAN [1]. However, despite its significant contributions, CycleGAN has encountered failure cases that we believe are related to GAN instability. These failures have prompted us to propose two general models aimed at alleviating these issues.
Furthermore, we align with recent findings in the literature that suggest the problem of image-to-image translation is ill-posed. This means that there might be multiple plausible solutions for a given input, making it challenging for models to accurately map one domain to another. By recognizing the ill-posed nature of this problem, we can better understand the limitations and devise approaches to overcome them.
The Role of GAN Instability
One of the main issues we address in our study is the GAN instability associated with image-to-image translation. GANs consist of a generator and a discriminator, where the generator attempts to generate realistic images, and the discriminator aims to differentiate between real and generated images. In the context of image-to-image translation, maintaining equilibrium between the generator and discriminator can be challenging.
GAN instability can lead to mode collapse, where the generator produces limited variations of outputs, failing to capture the full diversity of the target domain. This can result in poor image quality and inadequate translation performance. Our proposed models aim to address GAN instability to improve the effectiveness of image-to-image translation.
The Ill-Posed Nature of the Problem
In addition to GAN instability, we also recognize the ill-posed nature of image-to-image translation. The ill-posedness of a problem implies that there may be multiple plausible solutions or interpretations for a given input. In the context of image-to-image translation, this means that there can be multiple valid mappings between two domains.
The ill-posed nature of the problem poses challenges for models attempting to learn a single mapping between domains. Different approaches, such as incorporating additional information or constraints, may be necessary to achieve more accurate and diverse translations.
Future Directions
As we continue to explore the challenges and potential solutions in image-to-image translation, several future directions emerge. Addressing GAN instability remains a crucial focus, as improving the stability of adversarial training can lead to better image translation results.
Furthermore, understanding and tackling the ill-posed nature of the problem is essential for advancing the field. Exploring alternative learning frameworks, such as incorporating structured priors or leveraging additional data sources, may help overcome the limitations of a single mapping approach.
In conclusion, image-to-image translation holds great promise for various computer vision applications. By addressing GAN instability and recognizing the ill-posed nature of the problem, we can pave the way for more accurate and diverse translations. As researchers and practitioners delve deeper into this field, we anticipate the development of innovative approaches that push the boundaries of image-to-image translation and its impact on computer vision as a whole.
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by jsendak | Mar 15, 2024 | Computer Science
arXiv:2403.08806v1 Announce Type: cross
Abstract: Deepfake technology has raised concerns about the authenticity of digital content, necessitating the development of effective detection methods. However, the widespread availability of deepfakes has given rise to a new challenge in the form of adversarial attacks. Adversaries can manipulate deepfake videos with small, imperceptible perturbations that can deceive the detection models into producing incorrect outputs. To tackle this critical issue, we introduce Adversarial Feature Similarity Learning (AFSL), which integrates three fundamental deep feature learning paradigms. By optimizing the similarity between samples and weight vectors, our approach aims to distinguish between real and fake instances. Additionally, we aim to maximize the similarity between both adversarially perturbed examples and unperturbed examples, regardless of their real or fake nature. Moreover, we introduce a regularization technique that maximizes the dissimilarity between real and fake samples, ensuring a clear separation between these two categories. With extensive experiments on popular deepfake datasets, including FaceForensics++, FaceShifter, and DeeperForensics, the proposed method outperforms other standard adversarial training-based defense methods significantly. This further demonstrates the effectiveness of our approach to protecting deepfake detectors from adversarial attacks.
The Rise of Deepfakes: Addressing Authenticity and Adversarial Attacks
Deepfake technology has gained significant attention in recent years, raising concerns about the authenticity of digital content. As the availability of deepfakes becomes more widespread, detecting and combatting their harmful effects has become a priority. However, with the rise of deepfakes, a new challenge has emerged in the form of adversarial attacks.
Adversaries can manipulate deepfake videos by introducing small, imperceptible perturbations that deceive detection models into producing incorrect outputs. This poses a significant threat to the reliability of deepfake detection methods. To address this critical issue, the authors of the article introduce a novel approach called Adversarial Feature Similarity Learning (AFSL).
AFSL integrates three fundamental deep feature learning paradigms to effectively distinguish between real and fake instances. By optimizing the similarity between samples and weight vectors, the proposed approach aims to enhance the accuracy of deepfake detection models. Importantly, AFSL also maximizes the similarity between adversarially perturbed examples and unperturbed examples, irrespective of their real or fake nature.
Furthermore, the article introduces a regularization technique that emphasizes the dissimilarity between real and fake samples, enabling a clear separation between these two categories. This technique ensures that even with adversarial attacks, the deepfake detectors remain resilient and robust.
The efficacy of AFSL is validated through extensive experiments on popular deepfake datasets, including FaceForensics++, FaceShifter, and DeeperForensics. Compared to other standard defense methods based on adversarial training, the proposed approach outperforms them significantly. This demonstrates the effectiveness of AFSL in protecting deepfake detectors from adversarial attacks.
Multi-Disciplinary Nature
The concepts discussed in this article highlight the multi-disciplinary nature of deepfake detection and protection. The development of AFSL requires expertise in deep learning, feature extraction, adversarial attacks, and data regularization techniques. A successful defense against deepfakes necessitates a comprehensive understanding of various disciplines.
From a multimedia information systems perspective, deepfake detection and defense methods are crucial components. As multimedia content becomes increasingly pervasive and influential, ensuring its authenticity is of paramount importance. The development of robust techniques like AFSL contributes to the integrity and trustworthiness of multimedia information systems.
Additionally, deepfakes relate closely to the fields of Animations, Artificial Reality, Augmented Reality, and Virtual Realities. Deepfakes can be created using animation techniques and can be applied in virtual and augmented realities to fabricate realistic but synthetic experiences. However, techniques like AFSL play a vital role in ensuring the ethical use of deepfake technology and mitigating the potential harm caused by malicious actors.
In conclusion, the article presents Adversarial Feature Similarity Learning (AFSL) as an effective solution to tackle the challenge of adversarial attacks on deepfake detection models. The multi-disciplinary nature of deepfake detection and protection is evident in the integration of deep feature learning paradigms, adversarial attacks, regularization techniques, and extensive experimentation. The development of robust and reliable defense methods like AFSL contributes to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 15, 2024 | Computer Science
Lately, researchers in artificial intelligence have been focusing on the integration of language and vision, leading to the development of multimodal models. These models aim to seamlessly combine textual and visual information, providing a more comprehensive understanding of the world. While multimodal models have shown great promise in various tasks such as image captioning and visual question answering, they still face challenges in accurately interpreting images and answering questions in real-world scenarios.
This paper introduces a novel approach called Veagle, which enhances the multimodal capabilities of existing models by incorporating a unique mechanism. Inspired by the successes and insights of previous works, Veagle leverages a dynamic mechanism to project encoded visual information directly into the language model. This dynamic approach enables a more nuanced understanding of the intricate details present in visual contexts.
To assess the effectiveness of Veagle, comprehensive experiments are conducted on benchmark datasets, with a focus on tasks like visual question answering and image understanding. The results demonstrate a noticeable improvement of 5-6% in performance compared to existing models. This significant margin suggests that Veagle outperforms its counterparts and showcases its versatility and applicability beyond traditional benchmarks.
Expert Analysis
The integration of language and vision has been a challenging task in artificial intelligence. Multimodal models have emerged as a promising solution to bridge this gap, but their limitations in accurately interpreting visual information have hindered their performance in real-world scenarios. The introduction of Veagle offers a novel approach to address these limitations and enhance the capabilities of existing models.
By leveraging a dynamic mechanism to project encoded visual information into the language model, Veagle allows for a more nuanced understanding of visual contexts. This dynamic approach is inspired by previous successful works in the field, suggesting that it builds upon proven concepts and insights.
The comprehensive experiments conducted on benchmark datasets validate the effectiveness of Veagle. The improvement of 5-6% in performance compared to existing models indicates that Veagle surpasses its counterparts by a significant margin. This highlights the potential of Veagle to elevate the performance of multimodal models in tasks like visual question answering and image understanding.
Furthermore, the versatility and applicability of Veagle beyond traditional benchmarks signify its potential in real-world applications. As multimodal models continue to advance, Veagle’s unique approach can contribute to the development of more accurate and comprehensive models that seamlessly integrate textual and visual information.
In conclusion, the introduction of Veagle presents an exciting advancement in the field of multimodal models. Its dynamic mechanism for projecting visual information into the language model holds great promise in overcoming the limitations of existing models. The impressive performance improvement demonstrated in experiments solidifies Veagle’s position as a leading model in tasks involving the integration of language and vision.
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