by jsendak | Mar 14, 2025 | Computer Science
arXiv:2503.09852v1 Announce Type: new
Abstract: Speech-driven 3D facial animation is challenging due to the diversity in speaking styles and the limited availability of 3D audio-visual data. Speech predominantly dictates the coarse motion trends of the lip region, while specific styles determine the details of lip motion and the overall facial expressions. Prior works lack fine-grained learning in style modeling and do not adequately consider style biases across varying speech conditions, which reduce the accuracy of style modeling and hamper the adaptation capability to unseen speakers. To address this, we propose a novel framework, StyleSpeaker, which explicitly extracts speaking styles based on speaker characteristics while accounting for style biases caused by different speeches. Specifically, we utilize a style encoder to capture speakers’ styles from facial motions and enhance them according to motion preferences elicited by varying speech conditions. The enhanced styles are then integrated into the coarse motion features via a style infusion module, which employs a set of style primitives to learn fine-grained style representation. Throughout training, we maintain this set of style primitives to comprehensively model the entire style space. Hence, StyleSpeaker possesses robust style modeling capability for seen speakers and can rapidly adapt to unseen speakers without fine-tuning. Additionally, we design a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. Extensive qualitative and quantitative experiments on three public datasets demonstrate that our method outperforms existing state-of-the-art approaches.
Expert Commentary: Speech-driven 3D Facial Animation and the Multi-disciplinary Nature of the Concepts
The content discussed in this article revolves around the challenging task of speech-driven 3D facial animation. This topic is inherently multi-disciplinary, combining elements from various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Facial animation is a crucial component of many multimedia systems, including virtual reality applications and animated movies. To create realistic and expressive facial animations, it is important to accurately model the intricate details of lip motion and facial expressions. However, existing approaches often struggle to capture the fine-grained nuances of different speaking styles and lack the ability to adapt to unseen speakers.
The proposed framework, StyleSpeaker, addresses these limitations by explicitly extracting speaking styles based on speaker characteristics while considering the style biases caused by different speeches. By utilizing a style encoder, the framework captures speakers’ styles and enhances them based on motion preferences elicited by varying speech conditions. This integration of styles into the coarse motion features is achieved via a style infusion module that utilizes a set of style primitives to learn fine-grained style representation. The framework also maintains this set of style primitives throughout training to comprehensively model the entire style space.
In addition to style modeling, the framework introduces a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. These additional losses contribute to the overall accuracy of the animation and enhance its realism.
The experiments conducted on three public datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of both qualitative and quantitative measures. The combination of style modeling, motion-speech synchronization, and the adaptability to unseen speakers makes StyleSpeaker a promising framework for speech-driven 3D facial animation.
From a broader perspective, this research showcases the interconnectedness of different domains within multimedia information systems. The concepts of 3D facial animation, style modeling, and motion-speech synchronization are essential not only in the context of multimedia applications but also in fields like virtual reality, augmented reality, and artificial reality. By improving the realism and expressiveness of facial animations, this research contributes to the development of immersive experiences and realistic virtual environments.
Key takeaways:
- The content focuses on speech-driven 3D facial animation and proposes a novel framework called StyleSpeaker.
- StyleSpeaker explicitly extracts speaking styles based on speaker characteristics and accounts for style biases caused by different speeches.
- The framework enhances styles according to motion preferences elicited by varying speech conditions, integrating them into the coarse motion features.
- StyleSpeaker possesses robust style modeling capability and can rapidly adapt to unseen speakers without the need for fine-tuning.
- The framework introduces trend loss and local contrastive loss to improve motion-speech synchronization.
- The method outperforms existing state-of-the-art approaches in both qualitative and quantitative evaluations.
- The multi-disciplinary nature of the concepts involved showcases their relevance in the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Reference: Speech-driven 3D facial animation is challenging due to the diversity in speaking styles and the limited availability of 3D audio-visual data. Speech predominantly dictates the coarse motion trends of the lip region, while specific styles determine the details of lip motion and the overall facial expressions. Prior works lack fine-grained learning in style modeling and do not adequately consider style biases across varying speech conditions, which reduce the accuracy of style modeling and hamper the adaptation capability to unseen speakers. To address this, we propose a novel framework, StyleSpeaker, which explicitly extracts speaking styles based on speaker characteristics while accounting for style biases caused by different speeches. Specifically, we utilize a style encoder to capture speakers’ styles from facial motions and enhance them according to motion preferences elicited by varying speech conditions. The enhanced styles are then integrated into the coarse motion features via a style infusion module, which employs a set of style primitives to learn fine-grained style representation. Throughout training, we maintain this set of style primitives to comprehensively model the entire style space. Hence, StyleSpeaker possesses robust style modeling capability for seen speakers and can rapidly adapt to unseen speakers without fine-tuning. Additionally, we design a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. Extensive qualitative and quantitative experiments on three public datasets demonstrate that our method outperforms existing state-of-the-art approaches.
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by jsendak | Mar 14, 2025 | Computer Science
Article Analysis: Technology Empowering Vulnerable Populations
Technology has the power to revolutionize the way we live and interact with the world around us. However, it is important to recognize that vulnerable populations can face unique challenges and risks when it comes to embracing new technologies. This article highlights the significance of addressing these challenges while also exploring the possibilities of using technology to empower and support vulnerable communities.
Understanding the Risks
Vulnerable populations can include individuals who are economically disadvantaged, elderly, have disabilities, or belong to marginalized communities. These populations may face barriers in accessing and using technology, which can further exacerbate existing inequalities. For example, individuals with disabilities might struggle with inaccessible software or hardware, while those lacking digital skills may find it difficult to navigate online platforms.
In addition to access issues, vulnerabilities can also arise due to privacy and security concerns. With the increasing amount of personal information being exchanged online, it is crucial to ensure that the digital rights and privacy of vulnerable populations are protected. Without proper safeguards, these communities can become targets for scams, data breaches, or identity theft.
By understanding the risks faced by vulnerable populations, technology designers and researchers can create more inclusive and secure systems that minimize these challenges.
Opportunities for Empowerment
While there are risks associated with technology, it also presents unique opportunities to empower vulnerable populations. For instance, assistive technologies such as screen readers, speech recognition software, and wearable devices can enhance the quality of life for individuals with disabilities, enabling them to communicate and interact with their surroundings more effectively. Similarly, online platforms can provide financial inclusion for economically disadvantaged individuals by offering access to banking services, loans, and other essential resources.
Furthermore, technology can amplify the voices of marginalized communities, allowing them to connect with a wider audience and advocate for their rights. Social media platforms, for example, have played a crucial role in spreading awareness about various social issues and catalyzing social movements.
By harnessing the potential of technology, vulnerable populations can become active participants in shaping their own destinies and challenging systemic inequalities.
The Path Forward
To create technology that benefits everyone, including vulnerable populations, a collaborative effort between technology developers, researchers, policymakers, and community organizations is necessary. The following approaches can help pave the way forward:
- Inclusive Design: Technology should be designed with the needs of all users in mind, including those with disabilities or limited digital literacy. Involving diverse communities in the design process can provide valuable insights and lead to more inclusive solutions.
- Accessibility Standards: Implementing and enforcing accessibility standards is crucial to ensure that technology products and services are accessible to everyone. This requires consistent monitoring and validation by regulatory bodies.
- Digital Literacy Programs: Investing in digital literacy programs can empower vulnerable populations by providing them with the skills needed to navigate and utilize technology effectively. Community-based initiatives can play a crucial role in bridging the digital divide.
- Privacy and Data Protection: Strong privacy policies and data protection mechanisms should be in place to safeguard the digital rights of vulnerable populations. Transparency in data collection and usage, along with user consent, can help build trust and mitigate risks.
By adopting these approaches, we can foster a society where technology not only addresses the unique needs of vulnerable populations but also supports their empowerment, inclusion, and overall well-being.
Conclusion
Technology can be a powerful tool for positive change when harnessed appropriately. By acknowledging and addressing the challenges faced by vulnerable populations, we can ensure that technology serves as an equalizing force rather than a source of further inequality. The shift towards inclusive design, accessibility standards, digital literacy, and privacy protection is imperative in creating a future where technology empowers every individual, regardless of their vulnerabilities.
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by jsendak | Mar 12, 2025 | Computer Science
arXiv:2503.07911v1 Announce Type: new
Abstract: Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates redundant masks, and making post-processing more complex. Secondly, the CLIP model, mainly designed for global feature alignment in foundational models, often overlooks local objects crucial to remote sensing. This oversight leads to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Thirdly, both models have not been pre-trained on multi-scale aerial views, increasing the likelihood of detection failures. To tackle these challenges, we introduce the innovative VTPSeg pipeline, utilizing the strengths of Grounding DINO, CLIP, and SAM for enhanced open-vocabulary image segmentation. The Grounding DINO+(GD+) module generates initial candidate bounding boxes, while the CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes, ensuring that only pertinent objects are considered. Subsequently, these refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. Our VTPSeg is validated by experimental and ablation study results on five popular remote sensing image segmentation datasets.
Pixal-Level Segmentation in Remote Sensing: Enhanced Open-Vocabulary Image Segmentation
The field of remote sensing holds great potential for various applications, such as environmental monitoring, urban planning, and infrastructure management. However, extracting accurate and detailed information from remote sensing imagery remains a challenge. Pixal-level segmentation, which involves classifying each pixel in an image into specific objects or classes, is a crucial task in remote sensing.
In recent years, vision models like CLIP (Contrastive Language–Image Pretraining) and Segment Anything Model (SAM) have shown promising results in zero-shot segmentation tasks. These models leverage large-scale pretraining on diverse visual and textual data to learn powerful representations. However, when it comes to remote sensing, specific challenges need to be addressed to improve the accuracy and efficiency of segmentation.
Challenge 1: Redundant masks and post-processing complexity
The SAM model, although effective, often generates redundant masks due to the lack of clear prompt constraints. This means that multiple masks may be produced for a single object, making post-processing more complex. Finding a way to generate concise and accurate masks is vital for efficient segmentation in remote sensing imagery.
Challenge 2: Overlooking local objects
The CLIP model, originally designed for global feature alignment in foundational models, tends to overlook local objects that are crucial in remote sensing. This oversight can lead to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Addressing this issue is necessary to ensure that all relevant objects are properly identified and segmented.
Challenge 3: Lack of pre-training on multi-scale aerial views
Both the CLIP and SAM models have not been pretrained on multi-scale aerial views, which are common in remote sensing. This limitation increases the likelihood of detection failures, as the models may struggle to accurately segment objects at different scales. Incorporating pre-training on multi-scale aerial views is essential to enhance the robustness and effectiveness of segmentation models in remote sensing.
The Innovative VTPSeg Pipeline
To overcome these challenges, the researchers propose a novel pipeline called VTPSeg (Vision-Text Pretraining for Segmentation). VTPSeg combines the strengths of multiple models, namely Grounding DINO (GD+), CLIP Filter++ (CLIP++), and FastSAM, to achieve enhanced open-vocabulary image segmentation.
- The Grounding DINO+(GD+) module is responsible for generating initial candidate bounding boxes. This module leverages the power of pre-training on diverse visual and textual data to identify potential objects in remote sensing imagery.
- The CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes. By incorporating both visual and textual cues, CLIP++ ensures that only pertinent objects are considered for further segmentation.
- Finally, the refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. FastSAM takes into account the local objects often overlooked by CLIP, resulting in more accurate and detailed segmentation in multi-target remote sensing imagery.
Impact and Future Directions
The VTPSeg pipeline offers a significant advancement in pixel-level segmentation for remote sensing. By addressing the challenges specific to this field, the pipeline holds promise for improving the efficiency and accuracy of object segmentation in remote sensing imagery.
The multi-disciplinary nature of VTPSeg is noteworthy. It combines techniques from computer vision (CLIP and FastSAM) with language understanding (CLIP) and pre-training methodologies (Grounding DINO). This integration of diverse disciplines enhances the capabilities of the pipeline and opens up opportunities for cross-pollination of ideas between different fields.
Furthermore, the concept of enhanced open-vocabulary image segmentation, as demonstrated by VTPSeg, aligns with the wider field of multimedia information systems. Multimedia information systems deal with the management, retrieval, and analysis of multimedia data, including images and videos. Accurate segmentation is vital for efficient indexing and retrieval of multimedia content, making VTPSeg relevant not only for remote sensing but also for various multimedia applications.
Looking ahead, future research can explore the application of VTPSeg to other domains beyond remote sensing. The pipeline’s modular design allows for potential adaptability to different types of images and datasets. Additionally, incorporating more sophisticated techniques for post-processing and refinement of segmentation results could further improve the accuracy and usability of VTPSeg.
In conclusion, the VTPSeg pipeline presents a promising approach to enhance open-vocabulary image segmentation in remote sensing. By leveraging the strengths of different models and addressing the specific challenges of this field, VTPSeg contributes to the wider field of multimedia information systems and paves the way for future advancements in object recognition and segmentation.
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by jsendak | Mar 12, 2025 | Computer Science
Today, we delve into the world of logic puzzles with a fascinating puzzle called Evolomino. This pencil-and-paper puzzle, popularized by the Japanese publisher Nikoli, has gained quite a following, much like other puzzles such as Sudoku, Kakuro, Slitherlink, Masyu, and Fillomino.
The name “Evolomino” stems from the unique feature of this puzzle where the shape of the blocks gradually evolves based on pre-drawn arrows. This evolution concept adds an extra layer of complexity and intrigue to the solving process, making it a favorite among puzzle enthusiasts.
Proving NP-Completeness
In this article, we are presented with a remarkable assertion: the claim that determining whether there exists at least one solution to an Evolomino puzzle that satisfies all its rules is an NP-complete problem. To lend credibility to this statement, the authors provide a mathematical proof by reduction from 3-SAT.
For those unfamiliar with the term, NP-completeness is a classification in computational complexity theory, indicating that a problem is both in the complexity class NP (nondeterministic polynomial time) and is as “hard” as the hardest problems in NP. It is a significant milestone in a problem’s analysis, suggesting that efficient algorithms may be unlikely.
The reduction from 3-SAT, another well-known NP-complete problem, demonstrates that if we could efficiently solve Evolomino, we could also efficiently solve 3-SAT. This reduction establishes a clear link between the complexity of these two problems.
Implications of Parsimonious Reduction
One intriguing aspect of the authors’ proof is that it is parsimonious, a property often sought after in reducing problems. A parsimonious reduction means that the number of solutions to the original problem is preserved in the reduction process.
By demonstrating that counting the number of solutions to an Evolomino puzzle is #P-complete, the authors not only complement their NP-completeness proof but also shine a spotlight on the inherent complexity of counting solutions. #P-completeness signifies that determining the exact number of solutions is as difficult as counting solutions for the hardest problems in the class #P.
What Lies Ahead?
This research unveils the underlying complexity of solving and counting solutions for Evolomino puzzles. However, it also raises questions about potential extensions and variations of the puzzle.
Researchers and puzzle enthusiasts might explore the possibilities of developing approximation algorithms or heuristics that could offer efficient solutions for practical purposes. Additionally, this work could lay the groundwork for exploring variants of Evolomino with different constraints or additional puzzle elements.
Further research into the intricate connectedness between NP-completeness, #P-completeness, and other complexity classes could shed light on the vast landscape of computational challenges in the realm of logic puzzles.
Overall, this analysis demonstrates the impressive complexity of Evolomino puzzles and their connection to fundamental computational problems. Solving them and counting their solutions pose intriguing challenges and exciting research opportunities for both mathematicians and computer scientists alike.
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by jsendak | Mar 5, 2025 | Computer Science
arXiv:2503.00374v1 Announce Type: cross
Abstract: Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific structures. However, unlike conventional scenarios where multi-modal inputs share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics delineates molecular signatures through gene expression patterns. This inherent disparity introduces a major challenge in aligning them while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive features for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR’s superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and benefiting the cancer diagnosis.
MULTI-MODAL SELF-SUPERVISED LEARNING IN ONCOLOGY
In the field of oncology, the combination of histopathology and transcriptomics provides valuable insights into the morphology and molecular aspects of cancer. However, integrating these diverse data sources poses a challenge due to their inherent differences in characteristics. Conventional multi-modal integration methods tend to focus on aligning the modalities, but often fail to retain modality-specific structures. This is especially crucial in the case of histopathology and transcriptomics, where their distinct features offer unique and complementary information.
MIRROR: ADDRESSING THE CHALLENGES
To overcome these challenges, the researchers propose a novel multi-modal representation learning method called MIRROR. MIRROR takes into account both modality alignment and retention, providing a comprehensive solution for learning pathological representations.
MIRROR utilizes dedicated encoders to extract comprehensive features for each modality. This allows for the preservation of the specific attributes of histopathology and transcriptomics. Furthermore, MIRROR employs a modality alignment module to seamlessly integrate phenotype patterns and molecular profiles, bridging the gap between the morphology and gene expression patterns.
To ensure the uniqueness of each modality, MIRROR incorporates a modality retention module. This module safeguards the modality-specific attributes, preventing the loss of crucial information. Additionally, a style clustering module is incorporated to mitigate redundancy and enhance disease-relevant information. By modeling and aligning consistent pathological signatures within a clustering space, MIRROR maximizes the utility of the multi-modal data.
APPLICATIONS IN CANCER DIAGNOSIS
The effectiveness of MIRROR is extensively evaluated on TCGA cohorts for cancer subtyping and survival analysis. The results demonstrate its superior performance in constructing comprehensive oncological feature representations. By effectively integrating histopathology and transcriptomics, MIRROR provides valuable insights for cancer diagnosis.
IMPACT ON MULTIMEDIA INFORMATION SYSTEMS
The development of MIRROR contributes to the wider field of multimedia information systems. By integrating multi-modal data, MIRROR enhances the analysis and understanding of complex diseases, such as cancer. Its approach of balancing modality alignment and retention can be applied to other domains where diverse data sources need to be integrated. Additionally, MIRROR highlights the importance of multi-disciplinary collaboration, as it requires expertise from both the fields of oncology and information systems.
RELEVANCE TO ANIMATIONS, ARTIFICIAL REALITY, AUGMENTED REALITY, AND VIRTUAL REALITIES
While the focus of this article is on the application of MIRROR in oncology, the concepts and techniques discussed have relevance in the fields of animations, artificial reality, augmented reality, and virtual realities.
Animations, artificial reality, augmented reality, and virtual realities often involve the integration of different data sources and modalities to create immersive and interactive experiences. Just like in the case of histopathology and transcriptomics, the challenge lies in aligning and retaining the distinct characteristics of each modality. MIRROR’s approach of dedicated encoders, modality alignment, retention modules, and style clustering can be adapted to these fields to improve the integration and representation of multi-modal data.
In conclusion, the development of MIRROR and its applications in oncology demonstrate the importance of multi-modal self-supervised learning and the need for a balanced approach to modality alignment and retention. The concepts and techniques discussed in this article have far-reaching implications in the wider field of multimedia information systems, as well as in animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 5, 2025 | Computer Science
Automatically Evaluating the C1 Level of Basque Language Compositions
In this project, our main objective was to develop an automatic evaluator that determines whether Basque language compositions meet the C1 level. To achieve this goal, we obtained 10,000 transcribed compositions through a collaborative agreement between HABE and HiTZ, which were then used to train our system.
One of the major challenges we faced was the scarcity of data and the risk of overfitting the system. To overcome these challenges, we implemented various techniques such as Exploratory Data Analysis (EDA), Synthetic Composition Generation (SCL), and regularization. These techniques allowed us to make the most of the available data and create a robust and reliable evaluator.
We also conducted tests using different Language Models to analyze their behavior and performance in evaluating the C1 level of Basque compositions. The results of these tests provided valuable insights into the strengths and weaknesses of each model, helping us choose the most effective one for our system.
In addition to evaluating the accuracy and reliability of our system, we also conducted analyses of different system behaviors. This included measuring model calibration, which refers to the system’s ability to accurately predict the C1 level of a composition, and assessing the impact of artifacts, which could potentially influence the evaluation process.
Next Steps and Future Developments
While we have made significant progress in developing the automatic evaluator for Basque language compositions, there are still areas in which further improvements can be made.
- Continued Data Collection: Expanding the dataset used for training the system can help further improve its accuracy and generalization ability. This can be achieved through collaborations with educational institutions, language centers, and other organizations.
- Refinement of Language Models: Continuously refining and fine-tuning the Language Models used in the evaluation process can enhance the system’s ability to accurately assess the C1 level of compositions, especially considering the unique characteristics of the Basque language.
- Feedback Integration: Incorporating feedback from language experts and users of the system can provide valuable insights for system improvement and help address any potential biases or limitations.
- Expansion to Other Language Levels: Building upon the success of the C1 level evaluator, future developments could focus on extending the system to evaluate compositions at other proficiency levels, providing a comprehensive and versatile language evaluation tool.
Overall, the development of an automatic evaluator for Basque language compositions at the C1 level is a significant achievement. It has the potential to greatly assist language learners and educators in assessing their progress and providing targeted feedback for improvement. With further advancements and refinements, this system can contribute to the advancement of Basque language proficiency across various domains.
“The development of an automatic evaluator for Basque language compositions at the C1 level is an important step towards enhancing the language learning and evaluation process. This project provides a solid foundation for further advancements in automated language assessment, which can benefit learners and educators alike.” – [Your Name], Language Assessment Expert
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