by jsendak | Jan 11, 2024 | Computer Science
Analysis of the Proposed Adversarial Generative Network for Free-Hand Sketch Generation
Free-hand sketch recognition and generation have been popular tasks in recent years, with applications in various fields such as art, design, and computer graphics. However, there are specific domains, like the military field, where it is challenging to sample a large-scale dataset of free-hand sketches. As a result, data augmentation and image generation techniques often fail to produce images with diverse free-hand sketching styles, limiting the capabilities of recognition and segmentation tasks in related fields.
In this paper, the authors propose a novel adversarial generative network that addresses the limitations of existing techniques by accurately generating realistic free-hand sketches with various styles. The proposed model explores three key performance aspects: generating images with random styles sampled from a prior normal distribution, disentangling the painters’ styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes not present in the training set.
The authors demonstrate the strengths of their model through qualitative and quantitative evaluations on the SketchIME dataset. The evaluation includes assessing visual quality, content accuracy, and style imitation.
Key Contributions:
- Generation of Images with Various Styles: By leveraging a prior normal distribution, the model successfully synthesizes free-hand sketches with diverse styles. This capability is crucial for applications that require a wide range of artistic expressions and creative designs.
- Disentangling Painter Styles: The authors introduce a technique to disentangle the painting style from known free-hand sketches. This allows for targeted style generation based on specific characteristics or preferences, enabling users to generate images with distinct visual signatures.
- Handling Unknown Classes: The model demonstrates the ability to generate images of unknown classes that are not present during the training phase. This suggests potential applications in scenarios where it is challenging to obtain labeled data for every object or concept.
- Evaluation Metrics: The authors conduct both qualitative and quantitative evaluations to assess the performance of their model. This comprehensive evaluation provides valuable insights into the visual quality, content accuracy, and style imitation capabilities, establishing the effectiveness of the proposed approach.
The findings of this research are significant in advancing the field of free-hand sketch generation. The ability to accurately generate free-hand sketches with various styles has potential applications in areas such as visual design, gaming, and virtual reality. By enabling the disentanglement of painting styles, the model empowers users with fine-grained control over the generated content. Additionally, the capability to generate images of unknown classes expands the scope of the model’s applicability.
However, some questions may arise regarding the generalizability of the proposed model. The evaluation was mainly performed on the SketchIME dataset, and it would be valuable to assess its performance on other benchmark datasets and real-world scenarios. Moreover, further investigation could explore the interpretability of the generated styles and whether they align with recognized artistic schools or contemporary trends.
In conclusion, this paper introduces a novel adversarial generative network for free-hand sketch generation, showcasing impressive results in generating realistic sketches with diverse styles. The proposed model opens up opportunities for advancements in creative fields and has the potential for broader applications in image generation and design domains.
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by jsendak | Jan 11, 2024 | Computer Science
A key challenge in the widespread deployment and use of retired electric vehicle (EV) batteries for second-life (SL) applications is accurately estimating and monitoring their state of health (SOH). One of the main obstacles is the lack of knowledge about the historical usage of these battery packs, which can come from different sources.
However, a new online adaptive health estimation strategy has been introduced in this paper, aiming to overcome these challenges. This method relies solely on real-time operational data from SL batteries, allowing for on-the-field use. One of the key features of this strategy is that it guarantees bounded-input-bounded-output (BIBO) stability, ensuring reliable and accurate estimations.
In laboratory experiments using aged EV batteries, the proposed adaptive strategy has demonstrated its effectiveness. The estimator gains in this approach are dynamically adapted to suit the unique characteristics of each individual battery cell. This adaptability makes it a promising candidate for future SL battery management systems (BMS2).
This research is significant because it addresses a crucial issue in the second-life battery market. By providing accurate and real-time estimation of battery health, it enables better decision-making regarding the use and viability of retired EV batteries in various applications, such as energy storage systems or electric vehicle charging infrastructure.
In the future, it is possible that this online adaptive health estimation strategy could be further refined and integrated into battery management systems (BMS) used in electric vehicles. This would enhance the ability to assess the health and performance of EV batteries throughout their entire lifecycle, leading to improved efficiency and potentially extending their overall lifespan.
Furthermore, this research has the potential to contribute to the development of a circular economy for EV batteries. By utilizing retired batteries in second-life applications, their value and lifespan can be extended, reducing waste and promoting sustainability in the electric vehicle industry.
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by jsendak | Jan 11, 2024 | Computer Science
Conventional audio classification relied on predefined classes, lacking the
ability to learn from free-form text. Recent methods unlock learning joint
audio-text embeddings from raw audio-text pairs describing audio in natural
language. Despite recent advancements, there is little exploration of
systematic methods to train models for recognizing sound events and sources in
alternative scenarios, such as distinguishing fireworks from gunshots at
outdoor events in similar situations. This study introduces causal reasoning
and counterfactual analysis in the audio domain. We use counterfactual
instances and include them in our model across different aspects. Our model
considers acoustic characteristics and sound source information from
human-annotated reference texts. To validate the effectiveness of our model, we
conducted pre-training utilizing multiple audio captioning datasets. We then
evaluate with several common downstream tasks, demonstrating the merits of the
proposed method as one of the first works leveraging counterfactual information
in audio domain. Specifically, the top-1 accuracy in open-ended language-based
audio retrieval task increased by more than 43%.
The Multi-Disciplinary Nature of Audio Recognition and its Relationship to Multimedia Information Systems
In recent years, there has been a growing interest in developing advanced methods for audio recognition and understanding. This field has significant implications for various areas such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By leveraging the power of machine learning and natural language processing, researchers have made significant progress in training models to recognize sound events and sources from raw audio-text pairs.
One of the key challenges in audio recognition is the ability to learn from free-form text descriptions of audio. Conventional methods relied on predefined classes, limiting their ability to adapt to new scenarios and environments. However, recent advancements have unlocked the potential to learn joint audio-text embeddings, enabling models to understand and classify audio based on natural language descriptions.
This study takes this progress one step further by introducing the concepts of causal reasoning and counterfactual analysis in the audio domain. By incorporating counterfactual instances into the model, the researchers aim to improve the model’s ability to differentiate between similar sound events in alternative scenarios. For example, distinguishing between fireworks and gunshots at outdoor events can be a challenging task due to the similarities in sound characteristics.
To achieve this, the model considers both the acoustic characteristics of the audio and the sound source information from human-annotated reference texts. By leveraging counterfactual information, the model enhances its understanding of the underlying causal relationships and can make more accurate distinctions between different sound events.
The effectiveness of this model is validated through pre-training utilizing multiple audio captioning datasets. The evaluation of the model includes several common downstream tasks, such as open-ended language-based audio retrieval. The results demonstrate the merits of incorporating counterfactual information in the audio domain, with a remarkable increase in top-1 accuracy of over 43% for the audio retrieval task.
This research is highly multi-disciplinary, combining concepts from audio processing, natural language processing, and machine learning. By exploring the intersection of these fields, the researchers have paved the way for advancements in audio recognition and understanding. Moreover, the implications of this study extend beyond the realm of audio, with potential applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 11, 2024 | Computer Science
Expert Commentary: Improving Sales Enablement with Real-Time Question-Answering System
In today’s fast-paced sales environment, having access to relevant and up-to-date sales material/documentation is crucial for sales teams. This paper presents a real-time question-answering system designed specifically to aid sellers in retrieving relevant materials that they can share with customers or refer to during a call. By leveraging the power of language models and advanced machine learning techniques, the system showcases the potential of AI in improving sales enablement.
The authors demonstrate the effectiveness of their system by using the Seismic content repository as a large-scale example of diverse sales material. The system utilizes LLM (Language Model) embeddings to match sellers’ queries with relevant content from the repository. By designing elaborate prompts that make use of rich meta-features, such as document attributes and seller information, the system enhances the accuracy of content recommendations.
The architecture of the system employs a bi-encoder with a cross-encoder re-ranker, enabling it to return highly relevant content recommendations within seconds, even for large datasets. This speed of response is crucial for sales teams who need on-the-spot access to information during customer interactions.
Notably, the authors mention that their recommender system has been deployed as an AML (Azure Machine Learning) endpoint for real-time inference. This deployment ensures that sellers can access the system seamlessly within their workflow, further enhancing productivity and efficiency.
Integration into the Copilot interface, which is a part of the Dynamics CRM (Customer Relationship Management) tool, exemplifies how Microsoft recognizes the value of this solution. By incorporating the real-time question-answering system into their production version, Microsoft sellers benefit from enhanced sales enablement capabilities on a daily basis.
Looking ahead, this system represents a significant step forward in leveraging AI to improve sales enablement. Further advancements in natural language processing, including more sophisticated language models and better understanding of document context, could enhance the relevance and accuracy of content recommendations. Additionally, integrating user feedback and behavior data into the recommendation process could lead to personalized and context-aware recommendations, further empowering sales teams.
In conclusion, the real-time question-answering system presented in this paper showcases the potential of AI in revolutionizing sales enablement. By leveraging advanced techniques and integrating into existing sales tools, such as CRM systems, this solution brings tangible benefits to organizations. As AI continues to advance, it is clear that sales enablement will be significantly transformed, driving improved customer interactions and increased sales outcomes.
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by jsendak | Jan 10, 2024 | Computer Science
Artificial Intelligence (AI) has undoubtedly become an indispensable part of various applications across diverse domains. As AI continues to advance and permeate our lives, the need for explanations becomes increasingly crucial. In many cases, users without technical expertise find it challenging to trust and understand the decisions made by AI systems. This lack of transparency can hinder acceptance and adoption of AI technologies.
To address this issue, Explainable AI (XAI) has emerged as a field of research that aims to create AI systems capable of providing explanations for their decisions in a human-understandable manner. However, a significant drawback of existing XAI methods is that they are primarily designed for technical AI experts, making them overly complex and inaccessible to the average user.
In this paper, the authors present ongoing research focused on crafting XAI systems specifically tailored to guide non-technical users in achieving their desired outcomes. The aim is to enhance human-AI interactions and facilitate users’ understanding of complex AI systems.
The research objectives and methods employed are aimed at developing XAI systems that are not only explainable but also actionable for users. It is crucial for XAI systems to go beyond providing explanations and actually guide users towards achieving their desired outcomes. By doing so, XAI can bridge the gap between technical AI experts and non-technical consumers.
Key takeaways from the ongoing research highlight the importance of simplicity and accessibility in XAI systems. It is essential to strike a balance between providing meaningful explanations and avoiding overwhelming users with technical jargon. By ensuring that explanations are concise, clear, and tailored to the user’s specific context, XAI can truly enhance user understanding and trust.
The implications learned from user studies emphasize the positive impact of XAI on decision-making processes. Non-technical users feel more confident in their interactions with AI systems when they have access to understandable explanations. This increased trust can lead to greater acceptance and adoption of AI technologies in various domains.
Despite these advancements, there are open questions and challenges that the authors aim to address in future work. Enhancing human-AI collaboration requires further exploration in areas such as user-centered design, interpretability metrics, and iterative feedback loops. By addressing these challenges, XAI can continue to evolve and improve, ensuring that AI technologies are beneficial and accessible to users from all backgrounds.
In conclusion, this ongoing research on crafting XAI systems tailored to guide users in achieving desired outcomes through improved human-AI interactions offers valuable insights into the future of AI explainability. By emphasizing simplicity, actionability, and user-centric design, XAI has the potential to enhance transparency and trust, ultimately driving the widespread adoption of AI technologies in various domains.
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by jsendak | Jan 10, 2024 | Computer Science
Recently, the strong text creation ability of Large Language Models(LLMs) has
given rise to many tools for assisting paper reading or even writing. However,
the weak diagram analysis abilities of LLMs or Multimodal LLMs greatly limit
their application scenarios, especially for scientific academic paper writing.
In this work, towards a more versatile copilot for academic paper writing, we
mainly focus on strengthening the multi-modal diagram analysis ability of
Multimodal LLMs. By parsing Latex source files of high-quality papers, we
carefully build a multi-modal diagram understanding dataset M-Paper. By
aligning diagrams in the paper with related paragraphs, we construct
professional diagram analysis samples for training and evaluation. M-Paper is
the first dataset to support joint comprehension of multiple scientific
diagrams, including figures and tables in the format of images or Latex codes.
Besides, to better align the copilot with the user’s intention, we introduce
the `outline’ as the control signal, which could be directly given by the user
or revised based on auto-generated ones. Comprehensive experiments with a
state-of-the-art Mumtimodal LLM demonstrate that training on our dataset shows
stronger scientific diagram understanding performance, including diagram
captioning, diagram analysis, and outline recommendation. The dataset, code,
and model are available at
https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/PaperOwl.
Strengthening Multi-Modal Diagram Analysis for Scientific Academic Paper Writing
In the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, the ability to analyze diagrams has been a significant challenge for large language models (LLMs) and multimodal LLMs. However, a recent development in the creation ability of LLMs has paved the way for tools that assist in paper reading and writing. This article presents a novel approach that aims to enhance the diagram analysis abilities of multimodal LLMs, particularly in the context of scientific academic paper writing.
The authors have developed a dataset called M-Paper, which is designed to improve the multi-modal diagram understanding capabilities of LLMs. The dataset is created by parsing Latex source files of high-quality papers and aligning diagrams with related paragraphs. This allows for the construction of professional diagram analysis samples that can be used for training and evaluation purposes. Notably, M-Paper is the first dataset to support joint comprehension of multiple scientific diagrams, including figures and tables in the format of images or Latex codes.
To further enhance the alignment between the copilot and user’s intention, the authors introduce the concept of an ‘outline’ as a control signal. This outline can be provided by the user or generated automatically and then revised accordingly. The inclusion of this control signal aims to improve the overall performance of the copilot.
The research team conducted comprehensive experiments using a state-of-the-art multimodal LLM and trained it on their dataset. The results demonstrated a stronger scientific diagram understanding performance, encompassing diagram captioning, diagram analysis, and outline recommendation.
This work is highly interdisciplinary, bridging the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By addressing the limitations of LLMs and multimodal LLMs in diagram analysis, this research opens up new possibilities for leveraging the power of large language models in the context of academic paper writing. The availability of the dataset, code, and model on GitHub enables further research and development in this area.
Overall, the contribution of this research lies in its efforts to enhance the capabilities of LLMs and multimodal LLMs in understanding scientific diagrams, thereby assisting researchers and authors in the process of academic paper writing. By combining the strengths of multimedia information systems and language models, this work paves the way for more efficient and effective knowledge dissemination and communication in the scientific community.
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