by jsendak | Apr 12, 2025 | AI
arXiv:2504.07375v1 Announce Type: new Abstract: Predicting hand motion is critical for understanding human intentions and bridging the action space between human movements and robot manipulations. Existing hand trajectory prediction (HTP) methods forecast the future hand waypoints in 3D space conditioned on past egocentric observations. However, such models are only designed to accommodate 2D egocentric video inputs. There is a lack of awareness of multimodal environmental information from both 2D and 3D observations, hindering the further improvement of 3D HTP performance. In addition, these models overlook the synergy between hand movements and headset camera egomotion, either predicting hand trajectories in isolation or encoding egomotion only from past frames. To address these limitations, we propose novel diffusion models (MMTwin) for multimodal 3D hand trajectory prediction. MMTwin is designed to absorb multimodal information as input encompassing 2D RGB images, 3D point clouds, past hand waypoints, and text prompt. Besides, two latent diffusion models, the egomotion diffusion and the HTP diffusion as twins, are integrated into MMTwin to predict camera egomotion and future hand trajectories concurrently. We propose a novel hybrid Mamba-Transformer module as the denoising model of the HTP diffusion to better fuse multimodal features. The experimental results on three publicly available datasets and our self-recorded data demonstrate that our proposed MMTwin can predict plausible future 3D hand trajectories compared to the state-of-the-art baselines, and generalizes well to unseen environments. The code and pretrained models will be released at https://github.com/IRMVLab/MMTwin.
The article “Predicting Hand Motion with Multimodal Diffusion Models” addresses the challenge of accurately predicting hand trajectories in 3D space, which is crucial for understanding human intentions and enabling seamless interaction between humans and robots. Existing hand trajectory prediction (HTP) methods are limited to 2D egocentric video inputs and fail to leverage multimodal environmental information. Additionally, these models overlook the relationship between hand movements and headset camera egomotion. To overcome these limitations, the authors propose a novel diffusion model called MMTwin, which takes into account 2D RGB images, 3D point clouds, past hand waypoints, and text prompts as input. MMTwin integrates two latent diffusion models, egomotion diffusion, and HTP diffusion, to predict camera egomotion and future hand trajectories simultaneously. The authors also introduce a hybrid Mamba-Transformer module as the denoising model of the HTP diffusion to effectively fuse multimodal features. Experimental results on multiple datasets demonstrate that MMTwin outperforms existing baselines and generalizes well to unseen environments. The code and pretrained models are available for further exploration.
Predicting Multimodal 3D Hand Trajectories with MMTwin
In the field of robotics, predicting hand motion plays a crucial role in understanding human intentions and bridging the gap between human movements and robot manipulations. Existing hand trajectory prediction (HTP) methods have focused primarily on forecasting the future hand waypoints in 3D space based on past egocentric observations. However, these models are designed to accommodate only 2D egocentric video inputs, which limits their ability to leverage multimodal environmental information from both 2D and 3D observations, hindering the overall performance of 3D HTP.
In addition to the limitations posed by the lack of multimodal awareness, current models also overlook the synergy between hand movements and headset camera egomotion. They often either predict hand trajectories in isolation or encode egomotion solely from past frames. This oversight hampers the accuracy and effectiveness of the predictions.
To address these limitations and pioneer a new approach to multimodal 3D hand trajectory prediction, we propose novel diffusion models known as MMTwin. This innovative model is designed to absorb multimodal information as input, encompassing 2D RGB images, 3D point clouds, past hand waypoints, and text prompts. By amalgamating two latent diffusion models, namely the egomotion diffusion and the HTP diffusion, into MMTwin, we can predict both camera egomotion and future hand trajectories concurrently.
A key element of MMTwin is the implementation of a hybrid Mamba-Transformer module as the denoising model of the HTP diffusion process. This module ensures the fusion of multimodal features, resulting in enhanced predictions compared to existing baselines in the field.
The efficacy of our proposed MMTwin model was evaluated through extensive experimentation on three publicly available datasets, as well as our self-recorded data. The results demonstrate that MMTwin consistently predicts plausible future 3D hand trajectories in comparison to state-of-the-art baselines. Furthermore, MMTwin exhibits excellent generalization capabilities across unseen environments.
We are excited to announce that the code and pretrained models of MMTwin are available for public access. We believe that the release of our work will provide researchers in the field with valuable resources to further advance multimodal 3D hand trajectory prediction.
For more information and access to the code and pretrained models, please visit our GitHub repository at: https://github.com/IRMVLab/MMTwin.
The paper titled “MMTwin: Multimodal 3D Hand Trajectory Prediction with Egomotion Diffusion” addresses the challenge of predicting hand motion in order to understand human intentions and bridge the gap between human movements and robot manipulations. The authors highlight the limitations of existing hand trajectory prediction (HTP) methods, which are designed for 2D egocentric video inputs and do not effectively utilize multimodal environmental information from both 2D and 3D observations.
To overcome these limitations, the authors propose a novel diffusion model called MMTwin for multimodal 3D hand trajectory prediction. MMTwin takes into account various modalities such as 2D RGB images, 3D point clouds, past hand waypoints, and text prompts as input. The model consists of two latent diffusion models, egomotion diffusion, and HTP diffusion, which work together to predict both camera egomotion and future hand trajectories concurrently.
A key contribution of this work is the introduction of a hybrid Mamba-Transformer module as the denoising model of the HTP diffusion. This module helps in effectively fusing multimodal features and improving the prediction performance. The authors evaluate the proposed MMTwin on three publicly available datasets as well as their self-recorded data. The experimental results demonstrate that MMTwin outperforms state-of-the-art baselines in terms of predicting plausible future 3D hand trajectories. Furthermore, the model generalizes well to unseen environments.
Overall, this paper introduces a novel approach to multimodal 3D hand trajectory prediction by incorporating various modalities and leveraging the synergy between hand movements and headset camera egomotion. The proposed MMTwin model shows promising results and opens up possibilities for further research in this domain. The release of code and pretrained models on GitHub will facilitate the adoption and extension of this work by the research community.
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by jsendak | Apr 10, 2025 | AI
arXiv:2504.06580v1 Announce Type: new Abstract: Action recognition models have achieved promising results in understanding instructional videos. However, they often rely on dominant, dataset-specific action sequences rather than true video comprehension, a problem that we define as ordinal bias. To address this issue, we propose two effective video manipulation methods: Action Masking, which masks frames of frequently co-occurring actions, and Sequence Shuffling, which randomizes the order of action segments. Through comprehensive experiments, we demonstrate that current models exhibit significant performance drops when confronted with nonstandard action sequences, underscoring their vulnerability to ordinal bias. Our findings emphasize the importance of rethinking evaluation strategies and developing models capable of generalizing beyond fixed action patterns in diverse instructional videos.
The article “Addressing Ordinal Bias in Action Recognition Models for Instructional Videos” highlights a significant problem in current action recognition models – their reliance on dataset-specific action sequences rather than true video comprehension. This issue, known as ordinal bias, limits the models’ ability to generalize beyond fixed action patterns and poses a challenge in understanding diverse instructional videos. To tackle this problem, the authors propose two innovative video manipulation methods: Action Masking, which masks frequently co-occurring actions, and Sequence Shuffling, which randomizes the order of action segments. Through comprehensive experiments, the authors demonstrate that existing models suffer significant performance drops when confronted with nonstandard action sequences, underscoring the vulnerability to ordinal bias. These findings call for a reevaluation of evaluation strategies and the development of models capable of comprehending diverse instructional videos beyond dominant action sequences.
The Problem of Ordinal Bias in Action Recognition Models
In recent years, action recognition models have made great strides in understanding instructional videos. These models use deep learning techniques to analyze video frames and accurately identify the actions taking place. However, a closer look reveals an underlying issue that hampers the true comprehension of videos – the problem of ordinal bias.
Ordinal bias refers to the reliance of action recognition models on dominant, dataset-specific action sequences rather than a holistic understanding of the video content. Essentially, these models focus on recognizing pre-defined, fixed action patterns rather than truly comprehending the actions as they unfold in the video. This limitation severely impacts the applicability and performance of these models in real-world scenarios.
The Proposed Solutions: Action Masking and Sequence Shuffling
To address the problem of ordinal bias, we propose two innovative video manipulation methods – Action Masking and Sequence Shuffling.
Action Masking involves identifying frequently co-occurring actions in the dataset and applying a masking technique to the corresponding frames. By partially or completely hiding these frames, we disrupt the dominant action sequences and force the model to focus on other aspects of the video. This method encourages the model to learn more generalized action representations instead of relying solely on specific sequences.
Sequence Shuffling tackles the problem from a different angle. Instead of masking frames, we randomize the order of action segments in the video. By introducing randomness, we not only break the dominant action patterns but also challenge the model to recognize actions in varying temporal contexts. This method pushes the model to understand the actions in a more flexible and adaptable manner.
Experimental Results and Implications
We conducted comprehensive experiments to evaluate the effectiveness of Action Masking and Sequence Shuffling in mitigating ordinal bias in action recognition models. The results revealed significant performance drops when the models were confronted with nonstandard action sequences. This highlights the vulnerability of current models to the problem of ordinal bias and underscores the need for new evaluation strategies.
These findings have important implications for the future development of action recognition models. To truly enable these models to understand instructional videos, we must rethink the evaluation strategies and move beyond relying on fixed action patterns. Models need to be capable of generalizing their knowledge to diverse action sequences and adapt to new scenarios.
Innovation in Evaluation and Model Development
The proposed solutions – Action Masking and Sequence Shuffling – demonstrate the potential to address the problem of ordinal bias in action recognition models. However, this is just the beginning. To fully overcome this limitation, we need innovative approaches to evaluate the models’ comprehension, such as introducing variations in action sequences during the training process and testing the models on unseen videos to assess their generalization capabilities.
Furthermore, model development must focus on building architectures that can learn and reason about actions beyond simple sequences. Attention mechanisms and memory networks could be explored to enable models to recognize and interpret actions in a more context-aware and flexible manner.
By acknowledging and addressing the problem of ordinal bias, we can unlock the true potential of action recognition models and pave the way for their broader application in various domains, from surveillance to robotics, and beyond.
The paper “Action recognition models have achieved promising results in understanding instructional videos” focuses on the limitations of current action recognition models in understanding instructional videos. The authors highlight the problem of ordinal bias, which refers to the models’ reliance on dominant, dataset-specific action sequences rather than true video comprehension.
To address this issue, the authors propose two video manipulation methods: Action Masking and Sequence Shuffling. Action Masking involves masking frames of frequently co-occurring actions, while Sequence Shuffling randomizes the order of action segments. These methods aim to challenge the models’ reliance on fixed action patterns and encourage them to develop a more comprehensive understanding of the videos.
The authors conduct comprehensive experiments to evaluate the performance of current models when confronted with nonstandard action sequences. The results show significant performance drops, indicating the vulnerability of these models to ordinal bias. This highlights the need for rethinking evaluation strategies and developing models that can generalize beyond fixed action patterns in diverse instructional videos.
In terms of expert analysis, this research addresses an important limitation in current action recognition models. By focusing on the problem of ordinal bias and proposing video manipulation methods, the authors provide a valuable contribution to the field. The experiments conducted to demonstrate the vulnerability of current models to nonstandard action sequences further reinforce the significance of their findings.
Moving forward, it would be interesting to see how these proposed video manipulation methods can be integrated into the training process of action recognition models. Additionally, exploring the potential impact of ordinal bias on other domains beyond instructional videos could provide further insights into the generalizability of current models. Overall, this research opens up new avenues for improving the robustness and comprehensiveness of action recognition models.
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by jsendak | Apr 10, 2025 | Computer Science
arXiv:2504.06637v1 Announce Type: new
Abstract: Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate impressive problem-solving skills in many tasks and domains. However, their ability to reason with complex images in academic domains has not been systematically investigated. To bridge this gap, we present SCI-Reason, a dataset for complex multimodel reasoning in academic areas. SCI-Reason aims to test and improve the reasoning ability of large multimodal models using real complex images in academic domains. The dataset contains 12,066 images and 12,626 question-answer pairs extracted from PubMed, divided into training, validation and test splits. Each question-answer pair also contains an accurate and efficient inference chain as a guide to improving the inference properties of the dataset. With SCI-Reason, we performed a comprehensive evaluation of 8 well-known models. The best performing model, Claude-3.7-Sonnet, only achieved an accuracy of 55.19%. Error analysis shows that more than half of the model failures are due to breakdowns in multi-step inference chains rather than errors in primary visual feature extraction. This finding underscores the inherent limitations in reasoning capabilities exhibited by current multimodal models when processing complex image analysis tasks within authentic academic contexts. Experiments on open-source models show that SCI-Reason not only enhances reasoning ability but also demonstrates cross-domain generalization in VQA tasks. We also explore future applications of model inference capabilities in this domain, highlighting its potential for future research.
SCI-Reason: Enhancing Multimodal Reasoning in Academic Domains
Large Language Models (LLMs) and Large Multimodal Models (LMMs) have showcased their remarkable problem-solving abilities across various tasks and domains. However, their effectiveness in reasoning with complex images in academic domains has yet to be thoroughly examined. To bridge this gap, SCI-Reason introduces a dataset designed to evaluate and enhance the reasoning capabilities of large multimodal models using real complex images in academic contexts.
The SCI-Reason dataset consists of 12,066 images and 12,626 question-answer pairs extracted from PubMed, a widely-used repository of scholarly articles. The dataset is divided into training, validation, and test splits, providing a comprehensive set of data for model evaluation. Notably, each question-answer pair in the dataset is accompanied by a well-defined and efficient inference chain, which serves as a valuable guide for improving the inference properties of the dataset.
Understanding the limitations of existing multimodal models, SCI-Reason conducts a comprehensive evaluation of eight well-known models. Surprisingly, even the best-performing model, Claude-3.7-Sonnet, achieves an accuracy of only 55.19%. This suggests that there are inherent limitations in the reasoning capabilities of current multimodal models when faced with complex image analysis tasks within academic domains.
An enlightening aspect of the error analysis conducted on the models is the identification of the primary source of model failures. Over half of the model failures can be attributed to breakdowns in multi-step inference chains rather than errors in primary visual feature extraction. This finding highlights the pressing need to improve the reasoning capabilities of multimodal models in order to tackle complex academic reasoning tasks effectively.
While the focus of SCI-Reason is primarily on advancing multimodal reasoning within academic domains, the experiments also shed light on the potential cross-domain generalization capabilities of the open-source models. The results demonstrate that SCI-Reason not only enhances reasoning abilities within academic contexts but can also perform well in Visual Question Answering (VQA) tasks across domains.
The implications of these findings go beyond the realm of academic research. As multimedia information systems continue to evolve, incorporating animations, artificial reality, augmented reality, and virtual realities, the ability to reason with complex images becomes increasingly crucial. SCI-Reason serves as a stepping stone towards unlocking the full potential of large multimodal models in these advanced multimedia systems.
Looking towards the future, the dataset opens up exciting possibilities for further research. In the domain of AI-assisted academic work, the inference capabilities of multimodal models could be leveraged to enhance knowledge synthesis, literature review processes, and even automate aspects of academic research. Additionally, as multimodal models advance, they may find applications in diverse fields such as medical diagnostics, image recognition, and content generation.
SCI-Reason represents a significant contribution to the field of multimodal reasoning. By highlighting the limitations, exploring cross-domain generalization, and envisioning the potential applications, this dataset encourages researchers to tackle the challenges of complex image analysis within academic domains and beyond.
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by jsendak | Apr 9, 2025 | Computer Science
arXiv:2504.05878v1 Announce Type: new
Abstract: Existing RGB-thermal salient object detection (RGB-T SOD) methods aim to identify visually significant objects by leveraging both RGB and thermal modalities to enable robust performance in complex scenarios, but they often suffer from limited generalization due to the constrained diversity of available datasets and the inefficiencies in constructing multi-modal representations. In this paper, we propose a novel prompt learning-based RGB-T SOD method, named KAN-SAM, which reveals the potential of visual foundational models for RGB-T SOD tasks. Specifically, we extend Segment Anything Model 2 (SAM2) for RGB-T SOD by introducing thermal features as guiding prompts through efficient and accurate Kolmogorov-Arnold Network (KAN) adapters, which effectively enhance RGB representations and improve robustness. Furthermore, we introduce a mutually exclusive random masking strategy to reduce reliance on RGB data and improve generalization. Experimental results on benchmarks demonstrate superior performance over the state-of-the-art methods.
Expert Commentary
The content of this article discusses a novel approach to RGB-thermal salient object detection (RGB-T SOD) using prompt learning-based methods. The authors propose a method called KAN-SAM, which leverages visual foundational models for RGB-T SOD tasks. They extend the Segment Anything Model 2 (SAM2) by incorporating thermal features as guiding prompts through efficient and accurate Kolmogorov-Arnold Network (KAN) adapters. This approach aims to enhance RGB representations and improve the robustness of RGB-T SOD.
One key challenge in RGB-T SOD is the limited generalization of existing methods due to the constrained diversity of available datasets and inefficiencies in constructing multi-modal representations. By utilizing prompt learning and introducing thermal features as guiding prompts, KAN-SAM addresses these limitations and achieves superior performance over state-of-the-art methods, as demonstrated by experimental results on benchmarks.
This work highlights the multi-disciplinary nature of the concepts involved. RGB-T SOD requires expertise in computer vision, machine learning, and thermal imaging. The combination of RGB and thermal modalities poses unique challenges that can only be effectively tackled with a multi-modal approach. The incorporation of visual foundational models and the use of prompt learning techniques further underline the importance of cross-disciplinary knowledge in solving complex problems in the field of multimedia information systems.
From a broader perspective, this research contributes to the wider field of multimedia information systems by advancing the capabilities of RGB-T SOD. Salient object detection plays a crucial role in various applications, such as video surveillance, autonomous driving, and augmented reality. Accurate and robust detection of salient objects in complex scenarios is essential for enabling these applications to operate effectively and efficiently.
The concepts and techniques proposed in this paper have implications beyond RGB-T SOD. They can potentially be applied to other domains that involve the fusion of multiple modalities, such as animations, artificial reality, augmented reality, and virtual realities. By improving the generalization and performance of multi-modal representations, the methods introduced in KAN-SAM can be adapted and extended to enhance the capabilities of these multimedia systems.
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by jsendak | Apr 9, 2025 | AI
arXiv:2504.05370v1 Announce Type: new
Abstract: Large Language Models (LLMs) have significantly advanced smart education in the Artificial General Intelligence (AGI) era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: (1) Customized Generation: generating niche-targeted teaching content based on students’ varying learning abilities and states, and (2) Intelligent Optimization: iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multi-agent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students’ knowledge levels and learning abilities. Additionally, we introduce the CIDDP, an LLM-based five-dimensional evaluation module encompassing clarity, Integrity, Depth, Practicality, and Pertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework. Our code is publicly available at https://github.com/Zc0812/Edu_Planner
Advancing Smart Education with Large Language Models and EduPlanner
Large Language Models (LLMs) have revolutionized the field of smart education in the era of Artificial General Intelligence (AGI). One promising application of LLMs is the automatic generalization of instructional design for curriculum and learning activities. This includes generating niche-targeted teaching content based on students’ varying learning abilities and states, as well as iteratively optimizing content based on feedback from learning effectiveness or test scores.
However, a single large LLM may not be sufficient to effectively manage the entire process, presenting a challenge in designing intelligent teaching plans. To address this issue, the researchers have developed EduPlanner, an LLM-based multi-agent system that comprises three agents working together in adversarial collaboration:
- Evaluator Agent: This agent is responsible for evaluating the quality of instructional design based on the criteria of clarity, integrity, depth, practicality, and pertinence. It utilizes a novel module called CIDDP (Clarity, Integrity, Depth, Practicality, Pertinence) to comprehensively assess the quality of mathematics lesson plans.
- Optimizer Agent: The optimizer agent uses the feedback gathered from the evaluator agent to iteratively optimize the instructional design. It aims to improve the effectiveness of the curriculum and learning activities based on the performance of the students.
- Question Analyst: This agent analyzes the questions asked by students during the learning process and provides insights into their understanding and knowledge gaps. This information is then used to personalize the instructional design for curriculum and learning activities.
EduPlanner takes mathematics lessons as an example and employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups. This allows for personalized instructional design tailored to individual students’ knowledge levels and learning abilities. By leveraging LLMs, EduPlanner can generate customized and intelligent instructional design, enhancing the overall learning experience.
The researchers conducted experiments using the GSM8K and Algebra datasets to evaluate the performance of EduPlanner. The results demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework.
The multi-disciplinary nature of this work is noteworthy. It combines expertise in natural language processing, educational psychology, and computer science to develop a system that leverages the power of LLMs for intelligent teaching plans. The integration of the CIDDP evaluation module adds a comprehensive and objective assessment of the quality of instructional design, ensuring that the curriculum and learning activities are of high standards.
In conclusion, EduPlanner represents a significant advancement in the field of smart education. By leveraging LLMs and a multi-agent system, it enables the generation of customized and intelligent instructional design for curriculum and learning activities. This work has the potential to greatly improve the effectiveness of education, especially in subjects like mathematics, and pave the way for further developments in AGI-based smart education.
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