Enhance-A-Video: Better Generated Video for Free
11 Feb 2025 · Yang Luo, Xuanlei Zhao, Mengzhao Chen, Kaipeng Zhang, Wenqi Shao, Kai Wang, Zhangyang Wang, Yang You
Paper: https://arxiv.org/pdf/2502.07508v1.pdf
Code: https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video
11 Feb 2025 · Yang Luo, Xuanlei Zhao, Mengzhao Chen, Kaipeng Zhang, Wenqi Shao, Kai Wang, Zhangyang Wang, Yang You
DiT-based video generation has achieved remarkable results, but research into enhancing existing models remains relatively unexplored. In this work, we introduce a training-free approach to enhance the coherence and quality of DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing the cross-frame correlations based on non-diagonal temporal attention distributions. Thanks to its simple design, our approach can be easily applied to most DiT-based video generation frameworks without any retraining or fine-tuning. Across various DiT-based video generation models, our approach demonstrates promising improvements in both temporal consistency and visual quality. We hope this research can inspire future explorations in video generation enhancement.
Paper: https://arxiv.org/pdf/2502.07508v1.pdf
Code: https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video
#DataScience #ArtificialIntelligence #MachineLearning #PythonProgramming #DeepLearning #LLM #AIResearch #BigData #NeuralNetworks #DataAnalytics #NLP #AutoML #DataVisualization #ScikitLearn #Pandas #NumPy #TensorFlow #AIethics #PredictiveModeling #GPUComputing #OpenSourceAI #DeepSeek
https://t.iss.one/DataScienceT
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Accelerating Data Processing and Benchmarking of AI Models for Pathology
10 Feb 2025 · Andrew Zhang, Guillaume Jaume, Anurag Vaidya, Tong Ding, Faisal Mahmood
Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite of software tools for whole-slide image processing, foundation model benchmarking, and curated publicly available tasks. We anticipate that these resources will promote transparency, reproducibility, and continued progress in the field.
Paper: https://arxiv.org/pdf/2502.06750v1.pdf
Codes:
https://github.com/mahmoodlab/trident
https://github.com/mahmoodlab/patho-bench
10 Feb 2025 · Andrew Zhang, Guillaume Jaume, Anurag Vaidya, Tong Ding, Faisal Mahmood
Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite of software tools for whole-slide image processing, foundation model benchmarking, and curated publicly available tasks. We anticipate that these resources will promote transparency, reproducibility, and continued progress in the field.
Paper: https://arxiv.org/pdf/2502.06750v1.pdf
Codes:
https://github.com/mahmoodlab/trident
https://github.com/mahmoodlab/patho-bench
#DataScience #ArtificialIntelligence #MachineLearning #PythonProgramming #DeepLearning #LLM #AIResearch #BigData #NeuralNetworks #DataAnalytics #NLP #AutoML #DataVisualization #ScikitLearn #Pandas #NumPy #TensorFlow #AIethics #PredictiveModeling #GPUComputing #OpenSourceAI #DeepSeek
https://t.iss.one/DataScienceT
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