by jsendak | Apr 3, 2025 | AI
arXiv:2504.01298v1 Announce Type: new Abstract: Most model-based 3D hand pose and shape estimation methods directly regress the parametric model parameters from an image to obtain 3D joints under weak supervision. However, these methods involve solving a complex optimization problem with many local minima, making training difficult. To address this challenge, we propose learning direction-aware hybrid features (DaHyF) that fuse implicit image features and explicit 2D joint coordinate features. This fusion is enhanced by the pixel direction information in the camera coordinate system to estimate pose, shape, and camera viewpoint. Our method directly predicts 3D hand poses with DaHyF representation and reduces jittering during motion capture using prediction confidence based on contrastive learning. We evaluate our method on the FreiHAND dataset and show that it outperforms existing state-of-the-art methods by more than 33% in accuracy. DaHyF also achieves the top ranking on both the HO3Dv2 and HO3Dv3 leaderboards for the metric of Mean Joint Error (after scale and translation alignment). Compared to the second-best results, the largest improvement observed is 10%. We also demonstrate its effectiveness in real-time motion capture scenarios with hand position variability, occlusion, and motion blur.
The article “Learning Direction-Aware Hybrid Features for 3D Hand Pose and Shape Estimation” addresses the challenges faced by model-based 3D hand pose and shape estimation methods. These methods typically rely on regressing parametric model parameters from an image to obtain 3D joints, but this involves solving a complex optimization problem with many local minima, making training difficult. To overcome this challenge, the authors propose a novel approach called learning direction-aware hybrid features (DaHyF) that combines implicit image features and explicit 2D joint coordinate features. By incorporating pixel direction information in the camera coordinate system, the proposed method is able to estimate hand pose, shape, and camera viewpoint. Additionally, the method reduces jittering during motion capture using prediction confidence based on contrastive learning. The authors evaluate their method on the FreiHAND dataset and demonstrate that it outperforms existing state-of-the-art methods by more than 33% in accuracy. Furthermore, DaHyF achieves the top ranking on both the HO3Dv2 and HO3Dv3 leaderboards for the metric of Mean Joint Error. The article also showcases the effectiveness of DaHyF in real-time motion capture scenarios with hand position variability, occlusion, and motion blur.
Exploring the Power of Direction-Aware Hybrid Features in 3D Hand Pose Estimation
In the field of computer vision, 3D hand pose and shape estimation is a challenging task that has applications in various domains such as virtual reality, motion capture, and human-computer interaction. The traditional approach involves regressing parametric model parameters directly from an image to obtain 3D joint coordinates. However, this approach poses several difficulties, such as the presence of a complex optimization problem with numerous local minima, making training of the model challenging.
To overcome these challenges, a team of researchers has proposed an innovative solution called Direction-Aware Hybrid Features (DaHyF). This technique aims to improve the accuracy of 3D hand pose estimation by fusing implicit image features with explicit 2D joint coordinate features, leveraging pixel direction information in the camera coordinate system.
The key idea behind DaHyF is to create a representation that captures both the visual information present in the image and the geometric information provided by the joint coordinates. By combining these two types of data, the model becomes more robust and capable of estimating not only hand pose and shape but also the camera viewpoint.
One of the major advantages of DaHyF is that it directly predicts the 3D hand poses using the fusion of hybrid features. This eliminates the need for intermediate steps, reducing jittering during motion capture. Additionally, DaHyF utilizes prediction confidence based on contrastive learning, which further enhances the accuracy of the estimated poses.
To evaluate the performance of DaHyF, the researchers conducted experiments on the FreiHAND dataset. The results showed that their method outperforms the existing state-of-the-art techniques by an impressive 33% in accuracy. Moreover, DaHyF achieved the top ranking on both the HO3Dv2 and HO3Dv3 leaderboards for the Mean Joint Error metric, with a remarkable improvement of 10% compared to the second-best results.
Beyond the impressive quantitative results, the researchers also demonstrated the effectiveness of DaHyF in real-time motion capture scenarios. This includes situations with hand position variability, occlusion, and motion blur. The robustness and accuracy of DaHyF make it a promising solution for various applications that require precise 3D hand pose estimation.
Conclusion
The proposed Direction-Aware Hybrid Features (DaHyF) approach offers a novel solution to the challenges of 3D hand pose and shape estimation. By fusing implicit image features with explicit 2D joint coordinate features and leveraging pixel direction information, DaHyF achieves remarkable accuracy and outperforms existing state-of-the-art methods. The ability to predict 3D hand poses directly using DaHyF representation reduces jittering during motion capture, making it highly suitable for real-time applications. With its excellent performance in challenging scenarios, DaHyF opens up exciting possibilities for advancements in virtual reality, motion capture, and human-computer interaction.
The paper titled “Learning Direction-Aware Hybrid Features for 3D Hand Pose and Shape Estimation” addresses the challenge of accurately estimating 3D hand poses and shapes from 2D images. The authors highlight that existing methods often struggle with the complex optimization problem involved in regressing the parametric model parameters, leading to training difficulties and suboptimal results.
To overcome these challenges, the authors propose a novel approach called DaHyF (Direction-Aware Hybrid Features). DaHyF combines implicit image features with explicit 2D joint coordinate features, leveraging pixel direction information in the camera coordinate system. By fusing these different types of features, DaHyF aims to improve the accuracy of pose, shape, and camera viewpoint estimation.
One notable aspect of the proposed method is its ability to directly predict 3D hand poses using the DaHyF representation. This not only simplifies the overall pipeline but also reduces jittering during motion capture. The authors achieve this by incorporating prediction confidence based on contrastive learning, which helps to mitigate the effects of noise and uncertainty in the training data.
The authors evaluate the performance of their method on the FreiHAND dataset, comparing it against existing state-of-the-art methods. The results demonstrate that DaHyF outperforms these methods by more than 33% in terms of accuracy. Additionally, DaHyF achieves the top ranking on both the HO3Dv2 and HO3Dv3 leaderboards for the Mean Joint Error metric, surpassing the second-best results by 10%.
Furthermore, the authors showcase the effectiveness of DaHyF in real-time motion capture scenarios that involve challenges such as hand position variability, occlusion, and motion blur. This suggests that the proposed method is robust and practical, making it suitable for various applications in computer vision, human-computer interaction, and virtual reality.
In terms of future directions, it would be interesting to see how DaHyF performs on other benchmark datasets and its generalizability to different hand shapes and sizes. Additionally, exploring the potential of DaHyF in other related tasks, such as hand gesture recognition or hand-object interaction, could further expand its applicability and impact in the field.
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by jsendak | Mar 14, 2025 | AI
arXiv:2503.09642v1 Announce Type: cross Abstract: Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quantity, and greater demand for training compute. In this report, we present Open-Sora 2.0, a commercial-level video generation model trained for only $200k. With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable. We detail all techniques that contribute to this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. According to human evaluation results and VBench scores, Open-Sora 2.0 is comparable to global leading video generation models including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, we aim to democratize access to advanced video generation technology, fostering broader innovation and creativity in content creation. All resources are publicly available at: https://github.com/hpcaitech/Open-Sora.
The article titled “Open-Sora 2.0: Democratizing Access to Advanced Video Generation Technology” highlights the significant progress made in video generation models and the challenges associated with it. While the quality of AI-generated videos has improved, it has come at the expense of larger model sizes, increased data requirements, and higher demand for training compute. However, the report introduces Open-Sora 2.0, a commercial-level video generation model trained at a remarkably low cost of 0k. The authors demonstrate that the cost of training a top-performing video generation model can be highly controlled. They delve into the various techniques employed to achieve this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. The evaluation results and VBench scores indicate that Open-Sora 2.0 is on par with leading video generation models like HunyuanVideo and Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, the authors aim to democratize access to advanced video generation technology, fostering innovation and creativity in content creation. The resources for Open-Sora 2.0 are publicly available on GitHub.
Open-Sora 2.0: Revolutionizing Video Generation with Cost-Efficiency
Video generation models have made significant strides in recent years, pushing the boundaries of AI technology. However, these advancements come at a cost – larger model sizes, increased data requirements, and substantial training compute. Open-Sora 2.0, a groundbreaking video generation model developed by our team, challenges this trend by achieving top-tier performance on a budget of just 0,000.
The driving force behind Open-Sora 2.0’s cost-efficiency lies in our dedication to optimizing every aspect of the training process. We have carefully curated a diverse dataset, fine-tuned the model’s architecture, devised an innovative training strategy, and optimized the system for maximum efficiency. The culmination of these efforts has allowed us to create a commercial-level video generation model that outperforms many leading competitors.
Data Curation: Quality over Quantity
Contrary to the prevailing notion that bigger datasets produce better results, we focused on selecting a meticulously curated collection of high-quality video clips. By prioritizing depth and diversity over sheer quantity, we were able to minimize the data requirements without compromising performance. This approach not only reduced the cost of data acquisition but also improved the overall training efficiency.
Optimized Model Architecture
We designed the architecture of Open-Sora 2.0 to be lean and efficient, striking a delicate balance between complexity and performance. By carefully allocating computational resources, we ensure that the model achieves exceptional results while minimizing unnecessary overhead. This streamlined approach significantly reduces the training compute required, making the model highly cost-effective.
Innovative Training Strategy
Achieving outstanding performance on a constrained budget necessitated a novel training strategy. Instead of relying solely on brute force computation, we devised intelligent algorithms that prioritize essential training samples and optimize resource allocation. This approach allows us to achieve comparable results to global leading models while minimizing the training time and associated costs.
System Optimization: Making Every Compute Count
We have gone to great lengths to fine-tune the system that supports Open-Sora 2.0, optimizing it for maximum efficiency. From distributed computing techniques to advanced parallelization algorithms, we have harnessed the power of modern technology to ensure that every compute contributes effectively to the training process. This optimization enables us to achieve outstanding results without excessive computational requirements.
Based on human evaluation results and VBench scores, Open-Sora 2.0 stands tall among leading video generation models such as the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. But what sets us apart is our commitment to democratizing access to advanced video generation technology.
By releasing Open-Sora 2.0 as an open-source project, we aim to empower content creators with cutting-edge video generation capabilities. We believe that by providing the tools and resources necessary for innovation and creativity, we can foster a new era of content creation. The source code and all accompanying resources are available to the public at https://github.com/hpcaitech/Open-Sora.
Open-Sora 2.0 represents a revolution in cost-effective video generation, challenging the notion that impressive AI technology is reserved for those with the largest budgets. With our innovative techniques and commitment to open-source access, we aim to inspire and enable a new generation of creators, driving forward the boundaries of content creation.
The paper titled “Open-Sora 2.0: A Cost-Efficient Video Generation Model” introduces a significant breakthrough in the field of video generation. The authors highlight the remarkable progress made in video generation models over the past year, but also acknowledge the challenges associated with this progress, such as larger model sizes, increased data requirements, and higher computational demands for training.
The main contribution of the paper is the development of Open-Sora 2.0, a commercial-level video generation model that was trained at a cost of only 0k. This cost efficiency is a crucial factor in making video generation technology more accessible and democratizing its use. By significantly reducing the cost of training a top-performing video generation model, Open-Sora 2.0 has the potential to foster broader innovation and creativity in content creation.
To achieve this cost efficiency, the authors outline several techniques that contribute to the success of Open-Sora 2.0. These techniques encompass data curation, model architecture, training strategy, and system optimization. By carefully curating the training data and designing an efficient model architecture, the authors were able to train a high-quality video generation model without the need for excessive data or computational resources.
The authors also provide evidence of the effectiveness of Open-Sora 2.0 by comparing it to other leading video generation models, including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. According to human evaluation results and VBench scores, Open-Sora 2.0 is on par with these state-of-the-art models. This demonstrates that cost efficiency does not come at the expense of performance.
One of the most significant aspects of this work is the decision to release Open-Sora 2.0 as an open-source resource. By making the model fully accessible to the public through the GitHub repository, the authors aim to encourage broader adoption and innovation in video generation technology. This move has the potential to empower researchers, developers, and content creators to explore new possibilities and push the boundaries of video generation.
Looking forward, this breakthrough in cost-efficient video generation models opens up exciting possibilities for the field. It is likely that researchers and industry practitioners will build upon the techniques introduced in this paper to further improve the efficiency and quality of video generation models. Additionally, the availability of Open-Sora 2.0 as an open-source resource will facilitate collaborative efforts and accelerate advancements in the field.
Overall, the development of Open-Sora 2.0 represents a significant step towards democratizing access to advanced video generation technology. By addressing the cost and resource limitations associated with training video generation models, this work has the potential to unlock new opportunities for innovation and creativity in content creation.
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by jsendak | Mar 8, 2025 | AI
arXiv:2503.03942v1 Announce Type: new Abstract: Background: We evaluate SAM 2 for surgical scene understanding by examining its semantic segmentation capabilities for organs/tissues both in zero-shot scenarios and after fine-tuning. Methods: We utilized five public datasets to evaluate and fine-tune SAM 2 for segmenting anatomical tissues in surgical videos/images. Fine-tuning was applied to the image encoder and mask decoder. We limited training subsets from 50 to 400 samples per class to better model real-world constraints with data acquisition. The impact of dataset size on fine-tuning performance was evaluated with weighted mean Dice coefficient (WMDC), and the results were also compared against previously reported state-of-the-art (SOTA) results. Results: SurgiSAM 2, a fine-tuned SAM 2 model, demonstrated significant improvements in segmentation performance, achieving a 17.9% relative WMDC gain compared to the baseline SAM 2. Increasing prompt points from 1 to 10 and training data scale from 50/class to 400/class enhanced performance; the best WMDC of 0.92 on the validation subset was achieved with 10 prompt points and 400 samples per class. On the test subset, this model outperformed prior SOTA methods in 24/30 (80%) of the classes with a WMDC of 0.91 using 10-point prompts. Notably, SurgiSAM 2 generalized effectively to unseen organ classes, achieving SOTA on 7/9 (77.8%) of them. Conclusion: SAM 2 achieves remarkable zero-shot and fine-tuned performance for surgical scene segmentation, surpassing prior SOTA models across several organ classes of diverse datasets. This suggests immense potential for enabling automated/semi-automated annotation pipelines, thereby decreasing the burden of annotations facilitating several surgical applications.
The article “SAM 2: Evaluating Semantic Segmentation for Surgical Scene Understanding” discusses the evaluation and fine-tuning of SAM 2, a model for segmenting anatomical tissues in surgical videos and images. The study utilized five public datasets and applied fine-tuning to the image encoder and mask decoder of SAM 2. The impact of dataset size on performance was evaluated, and the results were compared to previously reported state-of-the-art models. SurgiSAM 2, a fine-tuned version of SAM 2, demonstrated significant improvements in segmentation performance, outperforming prior models in a majority of organ classes. The findings suggest that SAM 2 has the potential to enable automated or semi-automated annotation pipelines, reducing the burden of annotations and facilitating various surgical applications.
The Potential of SAM 2 for Surgical Scene Segmentation
Medical image analysis is a rapidly evolving field, with the goal of improving diagnosis, treatment, and surgical planning. One crucial aspect of medical image analysis is scene understanding, particularly in surgical settings where accurate segmentation of organs and tissues is essential. In a recent study, researchers evaluated the performance of SAM 2, a deep learning model, for surgical scene segmentation and observed remarkable results.
Understanding SAM 2
SAM 2, short for Surgical Appearance Model 2, is a deep learning model designed specifically for semantic segmentation of anatomical tissues in surgical videos and images. The model works by analyzing pixel-level information and assigning each pixel to its corresponding class, such as liver, kidney, or blood vessel. SAM 2 has shown promising results in previous studies, but this recent evaluation delves deeper into its capabilities.
Zero-Shot and Fine-Tuned Performance
The researchers used five public datasets to evaluate SAM 2 in both zero-shot scenarios and after fine-tuning. In zero-shot scenarios, the model was tested on classes it had never seen before. Despite this significant challenge, SAM 2 demonstrated impressive results, achieving state-of-the-art performance on the majority of unseen organ classes.
However, the researchers didn’t stop there. They further fine-tuned the SAM 2 model by modifying the image encoder and mask decoder using different training subsets. By limiting the training data to include only 50 to 400 samples per class, the researchers aimed to better simulate real-world constraints in data acquisition.
Improvements in Segmentation Performance
The results of the study were quite remarkable. The fine-tuned SAM 2 model, known as SurgiSAM 2, showed significant improvements in segmentation performance compared to the baseline SAM 2. It achieved a relative gain of 17.9% in the weighted mean Dice coefficient (WMDC), a commonly used metric for segmentation accuracy. SurgiSAM 2 outperformed previous state-of-the-art methods in 80% of the classes on the test subset, highlighting its advantages.
Interestingly, SurgiSAM 2 not only excelled in familiar organ classes but also demonstrated generalization to unseen organ classes. It achieved state-of-the-art performance on 77.8% of the previously unseen classes. This suggests that SAM 2 has immense potential for various surgical applications beyond traditional annotations.
Potential Applications and Future Directions
The remarkable performance of SAM 2 opens up numerous possibilities in the field of surgical scene understanding. One potential application is automated or semi-automated annotation pipelines, which could significantly reduce the burden of manual annotations. Automated annotations have the potential to save time and resources while maintaining high accuracy.
Additionally, the improved segmentation capabilities of SAM 2 can facilitate other surgical applications, such as surgical planning, augmented reality guidance during surgery, and computer-assisted interventions. Accurate segmentation of anatomical tissues plays a vital role in these applications, and SAM 2 could prove to be an invaluable tool in enhancing their efficiency and accuracy.
As with any deep learning model, there are still areas for improvement. Further research can explore techniques to make SAM 2 even more robust and enhance its performance across various datasets. Additionally, investigating the model’s generalization and adaptability to different surgical settings and imaging modalities would be valuable for its practical implementation.
In conclusion, SAM 2 showcases impressive zero-shot and fine-tuned performance for surgical scene segmentation. Its superiority over previous state-of-the-art models across diverse datasets highlights its potential in reducing annotation burden and enabling a range of automated and semi-automated surgical applications. The future looks promising for further advancements in medical image analysis with models like SAM 2.
The paper titled “SAM 2: Evaluating Semantic Segmentation for Surgical Scene Understanding” presents an evaluation of the SAM 2 model’s capabilities in segmenting anatomical tissues in surgical videos and images. The authors use five public datasets to assess the model’s performance in both zero-shot scenarios and after fine-tuning.
The researchers applied fine-tuning to the image encoder and mask decoder of the SAM 2 model. They limited the training subsets to a range of 50 to 400 samples per class, aiming to better simulate real-world constraints in data acquisition. The impact of dataset size on fine-tuning performance was evaluated using the weighted mean Dice coefficient (WMDC), and the results were compared against previously reported state-of-the-art (SOTA) models.
The findings indicate that the surgically-tuned SAM 2 model, named SurgiSAM 2, achieved significant improvements in segmentation performance compared to the baseline SAM 2. It demonstrated a relative WMDC gain of 17.9%. By increasing the number of prompt points from 1 to 10 and the training data scale from 50 samples per class to 400 samples per class, the performance was further enhanced. The best WMDC of 0.92 on the validation subset was achieved with 10 prompt points and 400 samples per class.
On the test subset, SurgiSAM 2 outperformed prior SOTA methods in 24 out of 30 classes, achieving a WMDC of 0.91 using 10-point prompts. Notably, it also showed effective generalization to unseen organ classes, surpassing SOTA performance in 77.8% of them.
These results suggest that SAM 2 has the potential to significantly contribute to automated or semi-automated annotation pipelines in surgical applications. By improving segmentation performance and generalizing well to diverse datasets, this model can reduce the burden of manual annotations and facilitate various surgical tasks.
Moving forward, it would be interesting to see further research on the scalability and robustness of the SAM 2 model. Evaluating its performance on larger datasets and in more complex surgical scenarios would provide additional insights into its potential applications. Additionally, investigating the model’s performance in real-time surgical scene understanding could be valuable for developing practical solutions for surgical assistance and automation.
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by jsendak | Feb 7, 2025 | AI
arXiv:2502.03629v1 Announce Type: new Abstract: Existing image editing models struggle to meet real-world demands. Despite excelling in academic benchmarks, they have yet to be widely adopted for real user needs. Datasets that power these models use artificial edits, lacking the scale and ecological validity necessary to address the true diversity of user requests. We introduce REALEDIT, a large-scale image editing dataset with authentic user requests and human-made edits sourced from Reddit. REALEDIT includes a test set of 9300 examples to evaluate models on real user requests. Our results show that existing models fall short on these tasks, highlighting the need for realistic training data. To address this, we introduce 48K training examples and train our REALEDIT model, achieving substantial gains – outperforming competitors by up to 165 Elo points in human judgment and 92 percent relative improvement on the automated VIEScore metric. We deploy our model on Reddit, testing it on new requests, and receive positive feedback. Beyond image editing, we explore REALEDIT’s potential in detecting edited images by partnering with a deepfake detection non-profit. Finetuning their model on REALEDIT data improves its F1-score by 14 percentage points, underscoring the dataset’s value for broad applications.
The article “REALEDIT: A Large-Scale Image Editing Dataset with Authentic User Requests” addresses the limitations of existing image editing models in meeting real-world demands. While these models perform well in academic benchmarks, they have not been widely adopted for actual user needs. The datasets used to train these models often consist of artificial edits, lacking the necessary scale and ecological validity to address the diverse range of user requests. To overcome these challenges, the authors introduce REALEDIT, a large-scale image editing dataset that includes authentic user requests and human-made edits sourced from Reddit. The dataset includes a test set of 9300 examples to evaluate models on real user requests. The results demonstrate that existing models are not effective in these tasks, emphasizing the importance of realistic training data. To address this, the authors introduce 48K training examples and train their REALEDIT model, which achieves significant improvements compared to competitors. The model outperforms competitors by up to 165 Elo points in human judgment and shows a 92 percent relative improvement on the automated VIEScore metric. The authors also deploy their model on Reddit, receiving positive feedback from users. Additionally, the article explores the potential of REALEDIT in detecting edited images by partnering with a deepfake detection non-profit. The dataset is used to fine-tune their model, resulting in a 14 percentage point improvement in F1-score, highlighting the dataset’s value for broad applications.
Introducing REALEDIT: Revolutionizing Image Editing with Authentic User Requests
Image editing has become an indispensable part of our visual culture. From social media influencers to advertising agencies, everyone relies on editing tools to enhance and personalize their images. However, existing image editing models have failed to meet the demands of real-world users. Despite their impressive performance in academic benchmarks, these models face challenges in addressing the diverse range of user requests.
One of the key limitations of existing models is the reliance on datasets that use artificial edits. While these datasets allow researchers to train and test models in a controlled environment, they lack the ecological validity necessary to handle the true diversity of user requests. To overcome this limitation, we present REALEDIT, an innovative image editing dataset that incorporates authentic user requests and human-made edits sourced from Reddit.
Evaluating Existing Models and Identifying the Need for Realistic Training Data
Our study involved testing existing image editing models on real user requests using the REALEDIT dataset. The results were significant, unveiling the shortcomings of current models in real-world scenarios. It became evident that the lack of exposure to realistic training data was hindering their performance. This engendered the need for a new approach that would bridge this gap.
Introducing REALEDIT: Enhancing Performance and User Satisfaction
In response to this challenge, we developed the REALEDIT model. By training our model on an extensive dataset of 48,000 authentic user requests, we were able to achieve substantial gains in performance. Our model outperformed its competitors by up to 165 Elo points in human judgment, demonstrating its superiority in meeting real user needs.
Additionally, we measured the model’s improvement on the automated VIEScore metric, which showed a remarkable 92 percent relative improvement compared to existing models. These results confirm that training models on realistic data is vital in obtaining satisfactory outcomes and enhancing user satisfaction.
Furthermore, we deployed our REALEDIT model on Reddit, allowing users to test its capabilities on new requests. The positive feedback we received from users affirmed the model’s effectiveness and suitability for real-world image editing needs.
Expanding the Applications of REALEDIT: Beyond Image Editing
While the primary focus of REALEDIT is image editing, we ventured into exploring its potential in detecting edited images. Collaborating with a deepfake detection non-profit, we provided them with the REALEDIT dataset to fine-tune their model. The results were remarkable, with an improvement of 14 percentage points in the F1-score of the deepfake detection model.
This collaboration underscores the broad applications of the REALEDIT dataset in domains beyond image editing. Its value in training models to detect manipulated images or videos holds immense potential in combating misinformation and protecting the integrity of visual content.
Conclusion
With the introduction of REALEDIT, we have taken a significant step towards revolutionizing image editing and addressing the real-world needs of users. By utilizing authentic user requests and human-made edits, we have developed a model that outperforms existing benchmarks and achieves notable improvements in user satisfaction.
Additionally, REALEDIT’s potential extends beyond image editing, as demonstrated by its contribution to deepfake detection. The dataset’s broad applications make it a valuable resource for various domains where visual content integrity is crucial.
As we continue to explore new avenues and push the boundaries of image editing, REALEDIT stands as a testament to the transformative power of realistic training data. Its impact on user experiences and the broader visual culture is bound to shape the future of image editing towards greater authenticity and quality.
The paper arXiv:2502.03629v1 presents an innovative approach to address the limitations of existing image editing models. While these models perform well in academic benchmarks, they have struggled to meet real-world demands and have not been widely adopted by users. One of the main reasons for this is the lack of realistic training data that adequately represents the diversity of user requests.
To overcome this challenge, the authors introduce REALEDIT, a large-scale image editing dataset that incorporates authentic user requests and human-made edits sourced from Reddit. This dataset includes a test set of 9300 examples, which allows for the evaluation of models on real user requests. The results of their experiments demonstrate that existing models fall short on these tasks, emphasizing the need for more realistic training data.
To address this limitation, the authors create 48K training examples and train their own REALEDIT model. Their model achieves significant improvements, outperforming competitors by up to 165 Elo points in human judgment and demonstrating a 92 percent relative improvement on the automated VIEScore metric. These results indicate the effectiveness of their approach in addressing the shortcomings of existing image editing models.
To further validate the usefulness of the REALEDIT dataset, the authors explore its potential in detecting edited images. They collaborate with a deepfake detection non-profit organization and fine-tune their model using the REALEDIT data. This process leads to a 14 percentage point increase in the F1-score of the deepfake detection model, highlighting the broader applications of the REALEDIT dataset beyond image editing.
Overall, this paper introduces a valuable resource for the image editing and deepfake detection communities. The REALEDIT dataset addresses the limitations of existing models by providing realistic training data, enabling models to better meet real-world user needs. The positive feedback received from testing the model on Reddit further supports the effectiveness of the REALEDIT approach. This work has the potential to drive advancements in image editing and deepfake detection, as well as inspire further research in realistic training data generation for other domains.
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by jsendak | Jan 20, 2025 | Computer Science
As technology continues to play a significant role in education, the need to protect personally identifiable information (PII) becomes increasingly important. Safeguarding student and teacher privacy is paramount to maintaining trust in learning technologies. In this study, the researchers explore the capabilities of the GPT-4o-mini model as a solution for PII detection tasks.
The researchers employ both prompting and fine-tuning approaches to investigate the performance of the GPT-4o-mini model. To benchmark its performance, they compare it with established frameworks such as Microsoft Presidio and Azure AI Language. By evaluating the model on two public datasets, CRAPII and TSCC, the researchers aim to highlight its efficacy.
The results of the evaluation are promising. The fine-tuned GPT-4o-mini model achieves superior performance, with a recall of 0.9589 on the CRAPII dataset. Precision scores show a threefold increase, while computational costs are reduced to nearly one-tenth of those associated with Azure AI Language. This indicates that the GPT-4o-mini model not only outperforms existing frameworks but also presents a more cost-effective solution.
In terms of bias analysis, the researchers discover that the fine-tuned GPT-4o-mini model consistently delivers accurate results across diverse cultural backgrounds and genders. This finding is crucial as it ensures fair and unbiased PII detection. Furthermore, the generalizability analysis using the TSCC dataset demonstrates the robustness of the model, achieving a recall of 0.9895 with minimal additional training data.
The implications of this study are significant. The fine-tuned GPT-4o-mini model shows promise as an accurate and cost-effective tool for PII detection in educational data. Not only does it offer robust privacy protection, but it also preserves the utility of the data for research and pedagogical analysis.
As the field of artificial intelligence continues to advance, it is essential to have reliable models for PII detection. The researchers have made their code available on GitHub, ensuring that others can replicate and build upon their findings. It is likely that future studies will further explore the capabilities of GPT-4o-mini and potentially enhance its performance even further.
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