by jsendak | Apr 13, 2025 | AI
arXiv:2504.07758v1 Announce Type: new Abstract: Polarization cameras can capture multiple polarized images with different polarizer angles in a single shot, bringing convenience to polarization-based downstream tasks. However, their direct outputs are color-polarization filter array (CPFA) raw images, requiring demosaicing to reconstruct full-resolution, full-color polarized images; unfortunately, this necessary step introduces artifacts that make polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP) prone to error. Besides, limited by the hardware design, the resolution of a polarization camera is often much lower than that of a conventional RGB camera. Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution, while polarized image super-resolution (PISR) methods, though designed to obtain high-resolution (HR) polarized images from the demosaicing results, tend to retain or even amplify errors in the DoP and AoP introduced by demosaicing artifacts. In this paper, we propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution, showing the ability to robustly obtain high-quality HR polarized images with more accurate DoP and AoP from a CPFA raw image in a direct manner. Experiments show our PIDSR not only achieves state-of-the-art performance on both synthetic and real data, but also facilitates downstream tasks.
The article “PIDSR: Polarized Image Demosaicing and Super-Resolution” addresses the challenges associated with polarization cameras and their outputs, known as color-polarization filter array (CPFA) raw images. These raw images require demosaicing to reconstruct full-resolution, full-color polarized images, but this step introduces artifacts that can lead to errors in polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP). Additionally, polarization cameras often have lower resolution compared to conventional RGB cameras. Existing methods for polarized image demosaicing (PID) cannot enhance resolution, and polarized image super-resolution (PISR) methods tend to amplify errors introduced by demosaicing artifacts. To overcome these limitations, the authors propose PIDSR, a joint framework that performs complementary polarized image demosaicing and super-resolution. The results demonstrate that PIDSR can obtain high-quality, high-resolution polarized images with more accurate DoP and AoP, showcasing its potential for improving downstream tasks.
The Power of PIDSR: Enhancing Polarized Images with Higher Resolution and Accuracy
Polarization cameras have revolutionized the field of imaging by allowing the capture of multiple polarized images in a single shot. This advancement brings convenience to polarization-based downstream tasks, opening up new possibilities for applications in various fields. However, despite their advantages, polarization cameras present certain challenges that need to be addressed.
The direct outputs of polarization cameras are color-polarization filter array (CPFA) raw images. To reconstruct full-resolution, full-color polarized images, a demosaicing process is required. Unfortunately, this necessary step introduces artifacts that can lead to errors in polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP).
Moreover, the resolution of polarization cameras is often lower than that of conventional RGB cameras due to hardware limitations. Existing polarized image demosaicing (PID) methods are unable to enhance resolution, and polarized image super-resolution (PISR) methods tend to amplify errors introduced by demosaicing artifacts.
In response to these challenges, we propose a novel joint framework called PIDSR (Polarized Image Demosaicing and Super-Resolution). Our framework aims to obtain high-quality, high-resolution polarized images with more accurate DoP and AoP from CPFA raw images in a direct manner.
In our proposed approach, PIDSR combines the processes of polarized image demosaicing and super-resolution. By integrating these two tasks, we are able to leverage their complementary nature and overcome the limitations of existing methods.
The results of our experiments show that PIDSR achieves state-of-the-art performance on both synthetic and real data. Not only does it provide enhanced resolution, but it also significantly improves the accuracy of the DoP and AoP parameters. This breakthrough not only benefits standalone polarized image applications but also facilitates downstream tasks that rely on precise polarization information.
Benefits of PIDSR:
- Obtains high-resolution polarized images from CPFA raw images
- Improves accuracy of polarization-related parameters (DoP and AoP)
- Reduces artifacts introduced by demosaicing process
- Enhances performance on both synthetic and real data
- Enables more robust downstream tasks reliant on polarization information
The potential applications of PIDSR are vast and diverse. Fields such as medical imaging, remote sensing, and computer vision can benefit from the enhanced capabilities provided by this framework. For example, in medical imaging, PIDSR can offer improved accuracy in polarization-based diagnostics or surgical procedures. Additionally, in remote sensing applications, PIDSR can enhance the quality and resolution of polarized image data for improved analysis and interpretation.
To unlock the full potential of polarization cameras, the development of advanced processing techniques is crucial. Our PIDSR framework represents a significant step forward in the field, offering a comprehensive solution to enhance polarized images with both higher resolution and accuracy. With further research and refinement, PIDSR has the potential to revolutionize various industries and drive innovation in polarization-based imaging.
The paper introduces a new framework called PIDSR (Polarized Image Demosaicing and Super-Resolution) that tackles the challenges faced by polarization cameras in capturing and reconstructing full-resolution, full-color polarized images. These cameras capture multiple polarized images with different polarizer angles in a single shot, but their direct outputs are color-polarization filter array (CPFA) raw images, which require demosaicing to reconstruct the final images. Unfortunately, demosaicing introduces artifacts that can lead to errors in polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP).
Moreover, polarization cameras often have lower resolutions compared to conventional RGB cameras due to hardware limitations. Existing demosaicing methods for polarized images are unable to enhance resolution, and polarized image super-resolution (PISR) methods, which aim to obtain high-resolution polarized images from demosaiced results, tend to retain or even amplify errors introduced by demosaicing artifacts.
In this context, PIDSR offers a joint framework that addresses both demosaicing and super-resolution, enabling the direct and robust generation of high-quality, high-resolution polarized images with more accurate DoP and AoP. The proposed framework not only achieves state-of-the-art performance on both synthetic and real data, but also facilitates downstream tasks that rely on polarized image analysis.
This research is significant as it addresses key limitations in polarization camera technology and provides a comprehensive solution for enhancing the quality and resolution of polarized images. By improving the accuracy of polarization-related parameters, PIDSR opens up possibilities for various applications, including object detection, material classification, and scene understanding. Future directions could involve further optimizing the framework for real-time processing and exploring its potential in specific domains, such as medical imaging or autonomous driving. Additionally, investigating the combination of PIDSR with other advanced image processing techniques, such as denoising or image fusion, could lead to further improvements in the quality and utility of polarized images.
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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 11, 2025 | AI
arXiv:2504.07335v1 Announce Type: new Abstract: We propose DLTPose, a novel method for 6DoF object pose estimation from RGB-D images that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions. DLTPose predicts per-pixel radial distances to a set of minimally four keypoints, which are then fed into our novel Direct Linear Transform (DLT) formulation to produce accurate 3D object frame surface estimates, leading to better 6DoF pose estimation. Additionally, we introduce a novel symmetry-aware keypoint ordering approach, designed to handle object symmetries that otherwise cause inconsistencies in keypoint assignments. Previous keypoint-based methods relied on fixed keypoint orderings, which failed to account for the multiple valid configurations exhibited by symmetric objects, which our ordering approach exploits to enhance the model’s ability to learn stable keypoint representations. Extensive experiments on the benchmark LINEMOD, Occlusion LINEMOD and YCB-Video datasets show that DLTPose outperforms existing methods, especially for symmetric and occluded objects, demonstrating superior Mean Average Recall values of 86.5% (LM), 79.7% (LM-O) and 89.5% (YCB-V). The code is available at https://anonymous.4open.science/r/DLTPose_/ .
The article “DLTPose: A Novel Approach for 6DoF Object Pose Estimation from RGB-D Images” introduces a new method that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions. The proposed method, DLTPose, predicts per-pixel radial distances to a set of minimally four keypoints, which are then used in a novel Direct Linear Transform (DLT) formulation to produce accurate 3D object frame surface estimates, leading to improved 6DoF pose estimation.
One of the key contributions of DLTPose is a novel symmetry-aware keypoint ordering approach, which addresses the challenges posed by object symmetries that often cause inconsistencies in keypoint assignments. Unlike previous methods that relied on fixed keypoint orderings, DLTPose leverages the multiple valid configurations exhibited by symmetric objects to enhance the model’s ability to learn stable keypoint representations.
The article presents extensive experiments conducted on benchmark datasets, including LINEMOD, Occlusion LINEMOD, and YCB-Video, demonstrating that DLTPose outperforms existing methods, particularly for symmetric and occluded objects. The results show superior Mean Average Recall values of 86.5% (LM), 79.7% (LM-O), and 89.5% (YCB-V) for DLTPose. The code for DLTPose is also made available for further exploration and use.
Unlocking Accurate 6DoF Object Pose Estimation with DLTPose
Advances in computer vision have brought us closer to achieving precise 6DoF (six degrees of freedom) object pose estimation from RGB-D images. However, existing methods often struggle with symmetric and occluded objects, leading to inconsistent and inaccurate results. In this article, we introduce DLTPose, a novel method that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions, addressing these challenges and setting a new benchmark for 6DoF pose estimation.
Redefining Keypoint Detection and Pose Estimation
DLTPose leverages the power of per-pixel radial distances to a set of minimally four keypoints. By predicting these distances, we capture detailed information about the object’s shape and structure. These distances are then fed into our Direct Linear Transform (DLT) formulation, which produces accurate 3D object frame surface estimates. This approach improves pose estimation by providing a more comprehensive representation of the object, surpassing the limitations of traditional keypoint methods.
Addressing Object Symmetries with a Novel Keypoint Ordering Approach
A major challenge in accurately estimating the pose of symmetric objects is assigning keypoints in a consistent and stable manner. Previous methods relied on fixed keypoint orderings, overlooking the multiple valid configurations exhibited by symmetric objects. DLTPose tackles this issue by introducing a novel symmetry-aware keypoint ordering approach.
Our ordering approach allows the model to learn stable keypoint representations by exploiting the various valid configurations of the object. By dynamically adapting the ordering of keypoints, DLTPose overcomes inconsistencies caused by object symmetries and significantly enhances the overall performance of the pose estimation model.
Outperforming Existing Methods on Benchmark Datasets
To validate the effectiveness of DLTPose, we conducted extensive experiments on benchmark datasets, including LINEMOD, Occlusion LINEMOD, and YCB-Video. The results unequivocally demonstrate the superiority of DLTPose, especially for symmetric and occluded objects.
DLTPose achieves Mean Average Recall (MAR) values of 86.5% on LINEMOD, 79.7% on Occlusion LINEMOD, and an impressive 89.5% on YCB-Video. These results clearly indicate the remarkable improvement over existing methods, highlighting the potential of DLTPose as a game-changer in the field of 6DoF object pose estimation.
Access the DLTPose Code
The code for DLTPose is openly available at https://anonymous.4open.science/r/DLTPose_. We encourage researchers and practitioners to explore DLTPose and further advance the capabilities of 6DoF object pose estimation.
In conclusion, DLTPose blends the strengths of sparse keypoint methods and dense pixel-wise predictions to deliver unparalleled accuracy in 6DoF object pose estimation. By incorporating a symmetry-aware keypoint ordering approach, DLTPose overcomes limitations posed by symmetric objects, producing consistent and robust results. With its impressive performance on benchmark datasets, DLTPose sets the stage for enhanced applications in robotics, augmented reality, and more. Get hands-on with DLTPose today and unlock the full potential of 6DoF object pose estimation.
The paper titled “DLTPose: A Novel Method for 6DoF Object Pose Estimation from RGB-D Images” introduces a new approach that aims to improve the accuracy and robustness of object pose estimation using a combination of sparse keypoint methods and dense pixel-wise predictions.
The authors propose DLTPose, a method that predicts per-pixel radial distances to a set of minimally four keypoints. These predicted distances are then used in their novel Direct Linear Transform (DLT) formulation to estimate accurate 3D object frame surfaces, which ultimately leads to better 6DoF pose estimation.
One notable contribution of this work is the introduction of a symmetry-aware keypoint ordering approach. This approach addresses the challenge of handling object symmetries that often cause inconsistencies in keypoint assignments. Unlike previous methods that relied on fixed keypoint orderings, which failed to account for multiple valid configurations exhibited by symmetric objects, the proposed ordering approach leverages the knowledge of object symmetries to enhance the model’s ability to learn stable keypoint representations.
To evaluate the performance of DLTPose, extensive experiments were conducted on benchmark datasets including LINEMOD, Occlusion LINEMOD, and YCB-Video. The results showed that DLTPose outperforms existing methods, particularly for symmetric and occluded objects. The Mean Average Recall (MAR) values achieved by DLTPose were 86.5% for LINEMOD, 79.7% for Occlusion LINEMOD, and 89.5% for YCB-Video. These results indicate the superior performance of DLTPose in accurately estimating the 6DoF pose of objects in challenging scenarios.
Overall, DLTPose presents a promising approach for 6DoF object pose estimation from RGB-D images. By combining the strengths of sparse keypoint methods and dense pixel-wise predictions, and incorporating a symmetry-aware keypoint ordering approach, DLTPose demonstrates improved accuracy and robustness compared to existing methods. The availability of the code further enhances the reproducibility and facilitates future research in this area.
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by jsendak | Apr 11, 2025 | AI
arXiv:2504.07131v1 Announce Type: new
Abstract: Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions.
Embedding Reliability Verification Constraints into Generation Expansion Planning
In generation planning, there is a challenge in managing the incompatible mathematical structures between stochastic production simulations and optimization models. This incompatibility creates difficulties in integrating reliability constraints into the planning process. However, an approach using a weighted oblique decision tree (WODT) technique has been proposed to solve this problem.
The proposed approach involves generating a generation mix dataset labeled with reliability assessment simulations for each planning year. This dataset is then used to train a WODT model. Using depth-first search technique, reliability-feasible regions are extracted and transformed into disjunctive constraints. These constraints are further converted into mixed-integer linear form using a convex hull modeling technique. Finally, the transformed constraints are embedded into a unit commitment-integrated generation expansion planning model.
This multi-disciplinary approach combines concepts from mathematical modeling, optimization, and reliability assessment. By leveraging the WODT technique, the proposed approach enables the integration of reliability constraints into generation expansion planning, ultimately leading to reliable and optimal planning solutions.
The effectiveness of this approach is demonstrated through a case study for the Electric Reliability Council of Texas (ERCOT) region. The long-term generation planning study validates the proposed approach, showing that it can achieve both reliability and optimality in the planning solutions.
Overall, this research contributes to the field of generation planning by addressing the challenge of integrating reliability constraints. The approach presented in this study provides a framework for effectively incorporating reliability assessment simulations into the planning process, leading to more robust and reliable generation expansion plans.
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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 | AI
arXiv:2504.06514v1 Announce Type: new
Abstract: We find that the response length of reasoning LLMs, whether trained by reinforcement learning or supervised learning, drastically increases for ill-posed questions with missing premises (MiP), ending up with redundant and ineffective thinking. This newly introduced scenario exacerbates the general overthinking issue to a large extent, which we name as the MiP-Overthinking. Such failures are against the “test-time scaling law” but have been widely observed on multiple datasets we curated with MiP, indicating the harm of cheap overthinking and a lack of critical thinking. Surprisingly, LLMs not specifically trained for reasoning exhibit much better performance on the MiP scenario, producing much shorter responses that quickly identify ill-posed queries. This implies a critical flaw of the current training recipe for reasoning LLMs, which does not encourage efficient thinking adequately, leading to the abuse of thinking patterns. To further investigate the reasons behind such failures, we conduct fine-grained analyses of the reasoning length, overthinking patterns, and location of critical thinking on different types of LLMs. Moreover, our extended ablation study reveals that the overthinking is contagious through the distillation of reasoning models’ responses. These results improve the understanding of overthinking and shed novel insights into mitigating the problem.
Analysis of the Effects of Ill-Posed Questions on Reasoning LLMs
The study presented in this article focuses on the response length of reasoning language models (LLMs) when presented with ill-posed questions that contain missing premises (MiP). The authors find that both reinforcement learning and supervised learning trained LLMs tend to demonstrate a significant increase in response length when faced with MiP questions, leading to redundant and ineffective thinking. This trend, which the authors term as MiP-Overthinking, represents a deviation from the expected “test-time scaling law” and highlights the prevalence of overthinking in LLMs.
One of the key insights from this study is the observation that LLMs not specifically trained for reasoning perform better on the MiP scenario. These models exhibit shorter responses that quickly identify the ill-posed nature of the queries. This suggests a critical flaw in the current training recipe for reasoning LLMs, which fails to sufficiently encourage efficient thinking and instead promotes thinking patterns that are prone to abuse.
The interdisciplinary nature of this study becomes apparent when considering the implications of overthinking and lack of critical thinking in LLMs. These models are designed to process and generate human-like language, which is inherently tied to cognitive processes. By investigating the reasons behind the failure of LLMs in handling MiP questions, the authors provide valuable insights into the relationships between language processing, reasoning, and critical thinking.
Fine-Grained Analysis and Ablation Study
To gain a deeper understanding of the phenomena observed, the authors conducted a fine-grained analysis of reasoning length, overthinking patterns, and the location of critical thinking in different types of LLMs. This analysis helps identify specific characteristics and patterns associated with overthinking, providing valuable information for mitigating the problem.
In addition, the authors conducted an extended ablation study, which revealed that overthinking can be contagious through the distillation of reasoning models’ responses. This finding has implications for the training and deployment of LLMs, as it suggests that the overthinking behavior of one model can influence and propagate to other models.
Implications and Mitigation Strategies
The findings of this study improve our understanding of overthinking in reasoning LLMs and offer insights into potential mitigation strategies. By shedding light on the flaws in the current training recipe, the authors pave the way for more efficient and effective thinking patterns in LLMs.
One possible mitigation strategy could involve incorporating explicit encouragement for efficient thinking during the training process of reasoning LLMs. By explicitly rewarding models for concise and accurate responses, the training recipe could steer LLMs away from overthinking and towards more efficient reasoning strategies.
Furthermore, the insights gained from the fine-grained analysis and ablation study can inform the development of novel architectures or modifications to existing LLMs that better handle ill-posed questions and reduce overthinking tendencies. This multi-disciplinary approach, combining insights from cognitive science, natural language processing, and machine learning, holds promise for improving the performance and reliability of reasoning LLMs.
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