by jsendak | May 27, 2024 | Computer Science
arXiv:2405.15026v1 Announce Type: new
Abstract: Peer review is a popular feedback mechanism in higher education that actively engages students and provides researchers with a means to assess student engagement. However, there is little empirical support for the durability of peer review, particularly when using data predictive modeling to analyze student comments. This study uses Na”ive Bayes modeling to analyze peer review data obtained from an undergraduate visual literacy course over five years. We expand on the research of Friedman and Rosen and Beasley et al. by focusing on the Na”ive Bayes model of students’ remarks. Our findings highlight the utility of Na”ive Bayes modeling, particularly in the analysis of student comments based on parts of speech, where nouns emerged as the prominent category. Additionally, when examining students’ comments using the visual peer review rubric, the lie factor emerged as the predominant factor. Comparing Na”ive Bayes model to Beasley’s approach, we found both help instructors map directions taken in the class, but the Na”ive Bayes model provides a more specific outline for forecasting with a more detailed framework for identifying core topics within the course, enhancing the forecasting of educational directions. Through the application of the Holdout Method and $mathrm{k}$-fold cross-validation with continuity correction, we have validated the model’s predictive accuracy, underscoring its effectiveness in offering deep insights into peer review mechanisms. Our study findings suggest that using predictive modeling to assess student comments can provide a new way to better serve the students’ classroom comments on their visual peer work. This can benefit courses by inspiring changes to course content, reinforcement of course content, modification of projects, or modifications to the rubric itself.
Analyzing Peer Review Data with Naïve Bayes Modeling
In higher education, peer review is widely used as a feedback mechanism to engage students and assess their engagement. However, there has been limited empirical evidence on the long-term effectiveness of peer review, especially when analyzing student comments using data predictive modeling. In this study, we explore the application of Naïve Bayes modeling to analyze peer review data from an undergraduate visual literacy course over a five-year period.
By building upon the research of Friedman and Rosen and Beasley et al., we focus on utilizing the Naïve Bayes model to analyze students’ remarks. The results of our study highlight the effectiveness of Naïve Bayes modeling, particularly when analyzing student comments based on parts of speech. We found that nouns emerged as the most prominent category in student comments, providing valuable insights into the topics students found important or relevant.
Furthermore, when examining students’ comments using the visual peer review rubric, we found that the lie factor, a measure of deception or misrepresentation, was the predominant factor. This finding suggests that students may struggle with accurately assessing their peers’ work and may be inclined to provide misleading feedback at times.
Comparing the Naïve Bayes model to Beasley’s approach, we discovered that both models are useful for instructors to map the directions taken in the class. However, the Naïve Bayes model offers a more specific outline for forecasting and provides a more detailed framework for identifying core topics within the course. This enhanced forecasting capability can greatly benefit educational directions, allowing instructors to make more informed decisions about changing course content, reinforcing important concepts, modifying projects, or even adjusting the rubric itself.
To validate the predictive accuracy of the Naïve Bayes model, we employed the Holdout Method and k-fold cross-validation with continuity correction. Our findings confirm the model’s effectiveness in offering deep insights into the peer review mechanisms. By utilizing predictive modeling to assess student comments, instructors can gain a new perspective on how to better serve students’ classroom comments on their visual peer work.
From a multi-disciplinary perspective, this study integrates concepts from the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By utilizing Naïve Bayes modeling, which is a machine learning technique widely used in various disciplines, we demonstrate its application in the context of visual peer review data analysis. This interdisciplinary approach highlights the potential for leveraging techniques from different fields to gain novel insights and enhance educational practices.
In conclusion, our study underscores the utility of Naïve Bayes modeling in analyzing peer review data, particularly for assessing student comments based on parts of speech and the visual peer review rubric. The findings provide valuable insights into student engagement and can inform improvements in course content, assignments, and assessment strategies. The multi-disciplinary nature of this study showcases the potential for cross-pollination of techniques from various fields, allowing for innovative approaches in educational research and practice.
Read the original article
by jsendak | May 27, 2024 | Computer Science
The Challenge of Out-Of-Distribution (OOD) Robustness in Deep Vision Models
Out-Of-Distribution (OOD) robustness is a crucial aspect in the deployment of deep vision models. These models have shown remarkable performance in recognizing and classifying objects within predefined categories. However, their inability to handle objects that are not part of the training data remains a significant challenge. Open-vocabulary object detection models aim to address this limitation by extending the capabilities of traditional object detection frameworks to recognize and classify objects beyond predefined categories.
In this study, the authors focus on investigating the OOD robustness of three recent open-vocabulary foundation object detection models: OWL-ViT, YOLO World, and Grounding DINO. By comparing the robustness of these models, they aim to provide insights into their performance in zero-shot scenarios.
The Importance of Robustness in Open-Vocabulary Object Detection
Robustness in open-vocabulary object detection models is critical for several reasons. Firstly, these models are often deployed in real-world scenarios where the presence of unseen or unexpected objects is common. For example, in autonomous driving applications, a vision model should be able to detect and respond to various objects, including those that were not part of the initial training data. Therefore, the ability of a model to handle OOD objects is crucial to ensure safe and reliable system performance.
Secondly, trust plays a vital role in the adoption and acceptance of deep vision models. If a model fails to detect or classify unfamiliar objects accurately, it can lead to reliability concerns and a loss of trust. By assessing the OOD robustness of open-vocabulary object detection models, this study contributes to increasing the trustworthiness of these models and instilling confidence in their performance.
A Comprehensive Comparison of Zero-Shot Capabilities
The authors conducted extensive experiments to compare the zero-shot capabilities of OWL-ViT, YOLO World, and Grounding DINO. They evaluated the performance of these models on the COCO-O and COCO-C benchmarks, which involve distribution shifts to highlight the challenges of OOD robustness.
By analyzing the results of these experiments, the study provides insights into the strengths and weaknesses of each model. These findings can help researchers and practitioners understand the limitations of existing open-vocabulary object detection models and guide further improvements in their robustness.
Implications and Future Directions
The availability of the source code for these models on GitHub is a significant contribution to the research community. It enables further analysis and experimentation, allowing researchers to build upon these models’ foundations and explore solutions to enhance OOD robustness.
Future research should focus on developing novel techniques and algorithms to improve the OOD robustness of open-vocabulary object detection models. This could involve leveraging transfer learning, domain adaptation, or incorporating additional contextual information to enhance the models’ generalization capabilities. Moreover, the evaluation of these models on more diverse and challenging datasets can provide a better understanding of their performance in real-world scenarios.
Overall, this study sheds light on the importance of OOD robustness in open-vocabulary object detection models and presents a comprehensive analysis of the zero-shot capabilities of OWL-ViT, YOLO World, and Grounding DINO. The findings from this study can serve as a benchmark for future research in improving the robustness of these models, ultimately advancing the deployment of deep vision models in real-world applications.
Read the original article
by jsendak | May 24, 2024 | Computer Science
arXiv:2405.14040v1 Announce Type: new
Abstract: Video storytelling is engaging multimedia content that utilizes video and its accompanying narration to attract the audience, where a key challenge is creating narrations for recorded visual scenes. Previous studies on dense video captioning and video story generation have made some progress. However, in practical applications, we typically require synchronized narrations for ongoing visual scenes. In this work, we introduce a new task of Synchronized Video Storytelling, which aims to generate synchronous and informative narrations for videos. These narrations, associated with each video clip, should relate to the visual content, integrate relevant knowledge, and have an appropriate word count corresponding to the clip’s duration. Specifically, a structured storyline is beneficial to guide the generation process, ensuring coherence and integrity. To support the exploration of this task, we introduce a new benchmark dataset E-SyncVidStory with rich annotations. Since existing Multimodal LLMs are not effective in addressing this task in one-shot or few-shot settings, we propose a framework named VideoNarrator that can generate a storyline for input videos and simultaneously generate narrations with the guidance of the generated or predefined storyline. We further introduce a set of evaluation metrics to thoroughly assess the generation. Both automatic and human evaluations validate the effectiveness of our approach. Our dataset, codes, and evaluations will be released.
Synchronized Video Storytelling: Generating Informative Narrations for Videos
Video storytelling is a captivating form of multimedia content that combines visual scenes with narration to engage the audience. However, creating synchronized narrations for recorded visual scenes can be a challenging task. Previous studies have made progress in the areas of dense video captioning and video story generation, but these methods do not necessarily provide synchronized narrations for ongoing visual scenes.
In this groundbreaking work, the researchers introduce a new task called Synchronized Video Storytelling, which aims to generate synchronous and informative narrations for videos. These narrations should effectively relate to the visual content, integrate relevant knowledge, and have an appropriate word count corresponding to the duration of each video clip. To ensure coherence and integrity, a structured storyline is introduced to guide the generation process.
To enable the exploration of this novel task, the researchers also introduce the E-SyncVidStory dataset, which comes with rich annotations. This dataset will serve as a benchmark for future research in the field of synchronized video storytelling.
It is noted that existing Multimodal Language and Vision Models (LLMs) are not effective in addressing this task in one-shot or few-shot settings. To overcome this challenge, the researchers propose a framework called VideoNarrator. This framework is designed to generate a storyline for input videos and simultaneously generate narrations with the guidance of the generated or predefined storyline.
Additionally, a comprehensive set of evaluation metrics is introduced to assess the effectiveness of the generation process. Automatic and human evaluations are conducted, both of which validate the efficacy of the proposed approach.
Overall, this research presents a significant advancement in the field of multimedia information systems, specifically in the areas of video storytelling, animations, artificial reality, augmented reality, and virtual realities. The multi-disciplinary nature of these concepts is evident in the task of synchronized video storytelling, which requires a deep understanding of both visual content and language generation. The proposed framework, VideoNarrator, can serve as a foundation for further advancements in generating informative narrations for videos. The release of the E-SyncVidStory dataset, along with the accompanying codes and evaluations, will undoubtedly facilitate future research in this exciting domain.
Read the original article
by jsendak | May 24, 2024 | Computer Science
Analysis of Sketch2Prototype: An AI Framework for Early-Stage Design Exploration
The Sketch2Prototype framework is an innovative AI-based solution that addresses the challenges faced during early-stage design exploration. By transforming hand-drawn sketches into a diverse set of 2D images and 3D prototypes, it enables designers to rapidly iterate and refine their designs.
The framework operates through three key stages: sketch-to-text, text-to-image, and image-to-3D. This multi-modal approach leverages the strengths of each modality to enhance the overall design process.
Sketch-to-Text: Unlocking Design Intent through Natural Language Processing
One of the remarkable features of Sketch2Prototype is its ability to convert hand-drawn sketches into text representations. By using natural language processing techniques, the framework interprets the design intent behind the sketch and generates textual descriptions.
This sketch-to-text stage allows designers to express their ideas in a more precise and structured manner. The textual representations serve as a valuable intermediate modality that bridges the gap between the abstract nature of sketches and the concrete requirements for generating 3D models. Moreover, the textual descriptions can be easily shared and analyzed for user feedback, enabling iterative design refinement.
Text-to-Image: Enriching Design Exploration with Visual Representations
The text representations generated in the previous stage are now used to obtain a diverse set of 2D images. The framework employs sophisticated text-to-image techniques to transform the textual descriptions into visual depictions of the design.
This stage significantly enhances the design exploration process by providing designers with a visual representation of their ideas. Visualizations offer a more tangible and intuitive understanding of the design, allowing for better evaluation and critique.
Image-to-3D: Transforming 2D Images into 3D Prototypes
The final stage of Sketch2Prototype involves the transformation of the 2D images into 3D prototypes. While this stage is crucial for fully realizing the design in a three-dimensional space, the article highlights some limitations in current image-to-3D techniques.
Further research and development are necessary to improve the accuracy and fidelity of image-to-3D conversion. Overcoming these limitations would enable designers to seamlessly transition from the early conceptual stages to the creation of manufacturable 3D models.
The Power of Multi-Modal Design Exploration
Sketch2Prototype demonstrates the value of a multi-modal approach in early-stage design exploration. By leveraging text, image, and 3D modalities, the framework expands the possibilities for design iteration and refinement.
The framework’s emphasis on text as an intermediate modality allows designers to express their design intent effectively. Textual descriptions provide clarity and precision, enabling better communication and collaboration among designers and stakeholders.
Furthermore, the ability to generate diverse and manufacturable 3D models through multiple modalities accelerates the design process. Designers can quickly explore different variations and evaluate their feasibility, ultimately leading to more informed decisions.
“Sketch2Prototype represents a significant step forward in early-stage design exploration. By harnessing the power of AI and multi-modal techniques, it empowers designers to unleash their creativity while maintaining a practical pathway towards manufacturability.”
In conclusion, Sketch2Prototype offers a compelling solution for enhancing early-stage design exploration. By integrating AI-based transformations of sketches into text, images, and 3D models, the framework bridges the gap between abstract ideas and concrete design requirements. While there are areas for improvement, such as image-to-3D techniques, the power of multi-modal design exploration and the ability to solicit user feedback through the text modality make this framework a valuable tool for designers. With further development and refinement, Sketch2Prototype has the potential to revolutionize the way we approach early-stage design.
Read the original article
by jsendak | May 22, 2024 | Computer Science
arXiv:2405.12775v1 Announce Type: new
Abstract: Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample’s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-$K$ parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://github.com/thuiar/UMC.
Understanding Multimodal Utterances with Unsupervised Multimodal Clustering (UMC)
In the field of multimedia information systems, understanding and analyzing multimodal utterances is crucial for enhancing human-machine interactions. Multimodal utterances consist of both verbal and nonverbal information, such as spoken words, facial expressions, gestures, and more. Traditional methods for discerning complex semantics in unsupervised scenarios have struggled to effectively leverage this nonverbal information.
This new research paper introduces a novel unsupervised multimodal clustering method called UMC, which makes significant strides in this field. UMC takes a unique approach to constructing augmentation views for multimodal data, allowing for pre-training and the establishment of well-initialized representations for subsequent clustering.
One of the key innovations of UMC is its strategy for dynamically selecting high-quality samples as guidance for representation learning. This selection process is based on the density of each sample’s nearest neighbors. By focusing on high-quality samples, UMC is able to refine the learning process and improve the overall clustering results.
In addition, UMC is equipped with the capability to automatically determine the optimal value for the top-K parameter in each cluster. This refinement further enhances the sample selection process and ensures that the clustering is performed as effectively as possible.
The authors of the paper evaluated UMC using benchmark multimodal intent and dialogue act datasets. The results showed remarkable improvements of 2-6% scores in clustering metrics compared to state-of-the-art methods. This marks a significant achievement in the field and highlights the potential of UMC for advancing our understanding of multimodal utterances.
The concepts presented in this paper go beyond the realm of multimodal clustering and have implications for various disciplines within the field of multimedia information systems. Animations, artificial reality, augmented reality, and virtual realities are all heavily reliant on effective understanding and synthesis of multimodal data. The advancements made by UMC in unsupervised semantic clustering can have a profound impact on the development of more immersive and interactive multimedia experiences.
In conclusion, this paper introduces UMC, a groundbreaking unsupervised multimodal clustering method that significantly improves the understanding of multimodal utterances. The innovative approaches employed by UMC, such as constructing augmentation views and dynamically selecting high-quality samples, pave the way for more effective and accurate clustering in unsupervised scenarios. The application of UMC extends beyond clustering and has implications for various disciplines within the wider field of multimedia information systems.
Read the original article
by jsendak | May 22, 2024 | Computer Science
Expert Commentary: Accelerating Stochastic Policy Gradient in Reinforcement Learning with Negative Momentum
In the field of reinforcement learning (RL), stochastic optimization algorithms like stochastic policy gradient (SPG) have shown great promise. However, one major challenge remains: how to quickly acquire an optimal solution for RL. In this article, the authors propose a new algorithm, SPG-NM, that addresses this issue by incorporating a novel technique called negative momentum (NM).
SPG-NM builds upon the classical SPG algorithm, but with the addition of NM. What makes this algorithm stand out is its unique approach to applying NM. Unlike existing techniques, SPG-NM utilizes a few hyper-parameters to optimize the performance. This distinction sets it apart from other algorithms in terms of computational complexity as well. SPG-NM performs at a similar level to modern SPG-type algorithms such as accelerated policy gradient (APG), which incorporates Nesterov’s accelerated gradient (NAG).
To evaluate the effectiveness of SPG-NM, the authors conducted experiments on two classical tasks: the bandit setting and Markov decision process (MDP). The results clearly demonstrate that SPG-NM achieves a faster convergence rate compared to state-of-the-art algorithms. This highlights the positive impact of NM in accelerating SPG for RL.
Furthermore, the authors conducted numerical experiments under different settings to assess the robustness of SPG-NM. The results confirm that the algorithm remains effective across different scenarios and certain crucial hyper-parameters. This finding is significant as it increases confidence in the practical application of SPG-NM.
Overall, this work presents a novel approach to accelerating the optimization process in reinforcement learning. By incorporating negative momentum into the stochastic policy gradient algorithm, SPG-NM demonstrates improved convergence rates and robustness. The findings pave the way for future advancements in RL algorithms and provide practitioners with a new tool for faster and more efficient RL optimization.
Read the original article
