✨Qworld: Question-Specific Evaluation Criteria for LLMs
📝 Summary:
Qworld is a new method that generates question-specific evaluation criteria for LLMs using recursive expansion trees. It decomposes questions into fine-grained criteria, enabling more insightful and granular assessment of LLM capabilities by adapting to each question's context. This approach reve...
🔹 Publication Date: Published on Mar 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23522
• PDF: https://arxiv.org/pdf/2603.23522
• Project Page: https://qworld.openscientist.ai/
• Github: https://github.com/mims-harvard/qworld
==================================
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#LLMEvaluation #LargeLanguageModels #AIResearch #NLP #MachineLearning
📝 Summary:
Qworld is a new method that generates question-specific evaluation criteria for LLMs using recursive expansion trees. It decomposes questions into fine-grained criteria, enabling more insightful and granular assessment of LLM capabilities by adapting to each question's context. This approach reve...
🔹 Publication Date: Published on Mar 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23522
• PDF: https://arxiv.org/pdf/2603.23522
• Project Page: https://qworld.openscientist.ai/
• Github: https://github.com/mims-harvard/qworld
==================================
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#LLMEvaluation #LargeLanguageModels #AIResearch #NLP #MachineLearning
arXiv.org
Qworld: Question-Specific Evaluation Criteria for LLMs
Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question's context. Binary scores and static rubrics fail to capture these...
❤1
✨The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
📝 Summary:
AI Scientist is an LLM system for automated scientific discovery. It handles ideas, experiments, papers, and simulated review. This system produces high-quality research for under $15, exceeding top conference standards.
🔹 Publication Date: Published on Aug 12, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2402.00854
• PDF: https://arxiv.org/pdf/2408.06292
• Github: https://github.com/ExtensityAI/benchmark/blob/main/src/evals/eval_computation_graphs.py#L551
🔹 Models citing this paper:
• https://huggingface.co/pradachan/AI-Scientist
• https://huggingface.co/priyanshmahant12/AI-Scientist-main
✨ Spaces citing this paper:
• https://huggingface.co/spaces/AUXteam/Critical_Code_Agent
==================================
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#AIScientist #AutomatedDiscovery #LLM #ScientificResearch #AIforScience
📝 Summary:
AI Scientist is an LLM system for automated scientific discovery. It handles ideas, experiments, papers, and simulated review. This system produces high-quality research for under $15, exceeding top conference standards.
🔹 Publication Date: Published on Aug 12, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2402.00854
• PDF: https://arxiv.org/pdf/2408.06292
• Github: https://github.com/ExtensityAI/benchmark/blob/main/src/evals/eval_computation_graphs.py#L551
🔹 Models citing this paper:
• https://huggingface.co/pradachan/AI-Scientist
• https://huggingface.co/priyanshmahant12/AI-Scientist-main
✨ Spaces citing this paper:
• https://huggingface.co/spaces/AUXteam/Critical_Code_Agent
==================================
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#AIScientist #AutomatedDiscovery #LLM #ScientificResearch #AIforScience
✨SpectralSplats: Robust Differentiable Tracking via Spectral Moment Supervision
📝 Summary:
SpectralSplats resolves vanishing gradients in 3D Gaussian Splatting tracking by optimizing in the frequency domain using spectral moments. This creates a global gradient basin of attraction, ensuring robust tracking even with severe misalignment. A frequency annealing schedule guides precise ali...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24036
• PDF: https://arxiv.org/pdf/2603.24036
• Project Page: https://avigailco.github.io/SpectralSplats/
==================================
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#3DTracking #GaussianSplatting #ComputerVision #Optimization #DifferentiableRendering
📝 Summary:
SpectralSplats resolves vanishing gradients in 3D Gaussian Splatting tracking by optimizing in the frequency domain using spectral moments. This creates a global gradient basin of attraction, ensuring robust tracking even with severe misalignment. A frequency annealing schedule guides precise ali...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24036
• PDF: https://arxiv.org/pdf/2603.24036
• Project Page: https://avigailco.github.io/SpectralSplats/
==================================
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#3DTracking #GaussianSplatting #ComputerVision #Optimization #DifferentiableRendering
✨Understanding the Challenges in Iterative Generative Optimization with LLMs
📝 Summary:
Generative optimization with LLMs is often brittle due to implicit design choices about artifact modification and learning evidence. These hidden decisions, such as starting artifact or batching, critically determine success across applications. Making these choices explicit is crucial for wider ...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23994
• PDF: https://arxiv.org/pdf/2603.23994
==================================
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#LLMs #GenerativeAI #Optimization #AIResearch #MachineLearning
📝 Summary:
Generative optimization with LLMs is often brittle due to implicit design choices about artifact modification and learning evidence. These hidden decisions, such as starting artifact or batching, critically determine success across applications. Making these choices explicit is crucial for wider ...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23994
• PDF: https://arxiv.org/pdf/2603.23994
==================================
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#LLMs #GenerativeAI #Optimization #AIResearch #MachineLearning
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✨The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
📝 Summary:
Generative video models suffer from inconsistent physical motion speeds due to varied training data. This work introduces Visual Chronometer, a tool that estimates a video's true physical frame rate from its visual dynamics. Correcting this significantly improves the naturalness of AI-generated v...
🔹 Publication Date: Published on Mar 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.14375
• PDF: https://arxiv.org/pdf/2603.14375
• Project Page: https://xiangbogaobarry.github.io/Pulse-of-Motion/
• Github: https://github.com/taco-group/Pulse-of-Motion
==================================
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#AIGeneratedVideo #ComputerVision #FrameRate #DeepLearning #AIResearch
📝 Summary:
Generative video models suffer from inconsistent physical motion speeds due to varied training data. This work introduces Visual Chronometer, a tool that estimates a video's true physical frame rate from its visual dynamics. Correcting this significantly improves the naturalness of AI-generated v...
🔹 Publication Date: Published on Mar 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.14375
• PDF: https://arxiv.org/pdf/2603.14375
• Project Page: https://xiangbogaobarry.github.io/Pulse-of-Motion/
• Github: https://github.com/taco-group/Pulse-of-Motion
==================================
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#AIGeneratedVideo #ComputerVision #FrameRate #DeepLearning #AIResearch
✨Internal Safety Collapse in Frontier Large Language Models
📝 Summary:
Frontier LLMs suffer Internal Safety Collapse, continuously generating harmful content under specific task conditions, even for benign tasks. A new framework triggers this vulnerability, yielding 95% safety failure rates and revealing inherent unsafe capabilities despite alignment efforts.
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23509
• PDF: https://arxiv.org/pdf/2603.23509
• Project Page: https://wuyoscar.github.io/ISC-Bench
• Github: https://github.com/wuyoscar/ISC-Bench
==================================
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#AISafety #LLM #AIAlignment #MachineLearning #AIResearch
📝 Summary:
Frontier LLMs suffer Internal Safety Collapse, continuously generating harmful content under specific task conditions, even for benign tasks. A new framework triggers this vulnerability, yielding 95% safety failure rates and revealing inherent unsafe capabilities despite alignment efforts.
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23509
• PDF: https://arxiv.org/pdf/2603.23509
• Project Page: https://wuyoscar.github.io/ISC-Bench
• Github: https://github.com/wuyoscar/ISC-Bench
==================================
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#AISafety #LLM #AIAlignment #MachineLearning #AIResearch
❤1
✨MACRO: Advancing Multi-Reference Image Generation with Structured Long-Context Data
📝 Summary:
To advance multi-reference image generation, this paper introduces MacroData, a large-scale dataset providing structured long-context supervision. It also proposes MacroBench, a standardized benchmark for evaluation. Fine-tuning on MacroData significantly improves generation performance.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25319
• PDF: https://arxiv.org/pdf/2603.25319
• Project Page: https://macro400k.github.io/
• Github: https://github.com/HKU-MMLab/Macro
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
To advance multi-reference image generation, this paper introduces MacroData, a large-scale dataset providing structured long-context supervision. It also proposes MacroBench, a standardized benchmark for evaluation. Fine-tuning on MacroData significantly improves generation performance.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25319
• PDF: https://arxiv.org/pdf/2603.25319
• Project Page: https://macro400k.github.io/
• Github: https://github.com/HKU-MMLab/Macro
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks
📝 Summary:
Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively ha...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24755
• PDF: https://arxiv.org/pdf/2603.24755
• Project Page: https://www.scbench.ai
• Github: https://github.com/SprocketLab/slop-code-bench
==================================
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#AICoding #Benchmarking #LLMAgents #SoftwareEngineering #CodeGeneration
📝 Summary:
Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively ha...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24755
• PDF: https://arxiv.org/pdf/2603.24755
• Project Page: https://www.scbench.ai
• Github: https://github.com/SprocketLab/slop-code-bench
==================================
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#AICoding #Benchmarking #LLMAgents #SoftwareEngineering #CodeGeneration
❤1
✨MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens
📝 Summary:
Memory Sparse Attention (MSA) enables large language models to process extremely long contexts with linear complexity and high efficiency through innovations like sparse attention and document-wise Ro...
🔹 Publication Date: Published on Mar 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23516
• PDF: https://arxiv.org/pdf/2603.23516
• Project Page: https://evermind.ai/blogs/breaking-the-100m-token-limit-msa-architecture-achieves-efficient-end-to-end-long-term-memory-for-llms
• Github: https://github.com/EverMind-AI/MSA
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Memory Sparse Attention (MSA) enables large language models to process extremely long contexts with linear complexity and high efficiency through innovations like sparse attention and document-wise Ro...
🔹 Publication Date: Published on Mar 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23516
• PDF: https://arxiv.org/pdf/2603.23516
• Project Page: https://evermind.ai/blogs/breaking-the-100m-token-limit-msa-architecture-achieves-efficient-end-to-end-long-term-memory-for-llms
• Github: https://github.com/EverMind-AI/MSA
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨Voxtral TTS
📝 Summary:
Voxtral TTS is a multilingual text-to-speech model that generates natural speech from short reference audio using a hybrid architecture combining semantic token generation and flow-matching for acoust...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25551
• PDF: https://arxiv.org/pdf/2603.25551
• Project Page: https://mistral.ai/news/voxtral-tts
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Voxtral TTS is a multilingual text-to-speech model that generates natural speech from short reference audio using a hybrid architecture combining semantic token generation and flow-matching for acoust...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25551
• PDF: https://arxiv.org/pdf/2603.25551
• Project Page: https://mistral.ai/news/voxtral-tts
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
📝 Summary:
LGTM is a feed-forward framework that enables high-fidelity 4K novel view synthesis by predicting compact Gaussian primitives with per-primitive textures, decoupling geometric complexity from renderin...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25745
• PDF: https://arxiv.org/pdf/2603.25745
• Project Page: https://yxlao.github.io/lgtm/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
LGTM is a feed-forward framework that enables high-fidelity 4K novel view synthesis by predicting compact Gaussian primitives with per-primitive textures, decoupling geometric complexity from renderin...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25745
• PDF: https://arxiv.org/pdf/2603.25745
• Project Page: https://yxlao.github.io/lgtm/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
📝 Summary:
LGTM is a feed-forward framework that enables high-fidelity 4K novel view synthesis by predicting compact Gaussian primitives with per-primitive textures, decoupling geometric complexity from renderin...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25745
• PDF: https://arxiv.org/pdf/2603.25745
• Project Page: https://yxlao.github.io/lgtm/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
LGTM is a feed-forward framework that enables high-fidelity 4K novel view synthesis by predicting compact Gaussian primitives with per-primitive textures, decoupling geometric complexity from renderin...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25745
• PDF: https://arxiv.org/pdf/2603.25745
• Project Page: https://yxlao.github.io/lgtm/
==================================
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arXiv.org
Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their...
❤1
✨Vega: Learning to Drive with Natural Language Instructions
📝 Summary:
Vega is a unified Vision-Language-World-Action model that combines autoregressive and diffusion paradigms for instruction-based driving planning and trajectory generation. AI-generated summary Vision-...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25741
• PDF: https://arxiv.org/pdf/2603.25741
• Project Page: https://zuosc19.github.io/Vega/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Vega is a unified Vision-Language-World-Action model that combines autoregressive and diffusion paradigms for instruction-based driving planning and trajectory generation. AI-generated summary Vision-...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25741
• PDF: https://arxiv.org/pdf/2603.25741
• Project Page: https://zuosc19.github.io/Vega/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
📝 Summary:
Intern-S1-Pro is a one-trillion-parameter scientific multimodal foundation model that enhances general and scientific capabilities through advanced agent functionalities and specialized task mastery a...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25040
• PDF: https://arxiv.org/pdf/2603.25040
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Intern-S1-Pro is a one-trillion-parameter scientific multimodal foundation model that enhances general and scientific capabilities through advanced agent functionalities and specialized task mastery a...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25040
• PDF: https://arxiv.org/pdf/2603.25040
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models
📝 Summary:
Multi-Resolution Fusion enables vision foundation models to leverage complementary inductive biases from different resolutions without architectural modifications or additional training. AI-generated ...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25744
• PDF: https://arxiv.org/pdf/2603.25744
• Project Page: https://MuRF-VFM.github.io
• Github: https://github.com/orgs/MuRF-VFM
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Multi-Resolution Fusion enables vision foundation models to leverage complementary inductive biases from different resolutions without architectural modifications or additional training. AI-generated ...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25744
• PDF: https://arxiv.org/pdf/2603.25744
• Project Page: https://MuRF-VFM.github.io
• Github: https://github.com/orgs/MuRF-VFM
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment
📝 Summary:
A multimodal framework for biological species identification that aligns visual, textual, and acoustic data to learn unified representations capturing species-level semantics beyond traditional taxono...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23883
• PDF: https://arxiv.org/pdf/2603.23883
• Project Page: https://dahlian00.github.io/BioVITA_Page/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A multimodal framework for biological species identification that aligns visual, textual, and acoustic data to learn unified representations capturing species-level semantics beyond traditional taxono...
🔹 Publication Date: Published on Mar 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23883
• PDF: https://arxiv.org/pdf/2603.23883
• Project Page: https://dahlian00.github.io/BioVITA_Page/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol
📝 Summary:
FinMCP-Bench is a comprehensive benchmark for evaluating large language models on financial problem-solving through tool invocation and reasoning across multiple complexity levels. AI-generated summar...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24943
• PDF: https://arxiv.org/pdf/2603.24943
• Project Page: https://github.com/aliyun/qwen-dianjin
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
FinMCP-Bench is a comprehensive benchmark for evaluating large language models on financial problem-solving through tool invocation and reasoning across multiple complexity levels. AI-generated summar...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24943
• PDF: https://arxiv.org/pdf/2603.24943
• Project Page: https://github.com/aliyun/qwen-dianjin
==================================
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❤1
✨Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors
📝 Summary:
Deep learning model for precipitation nowcasting that combines radar imagery with meteorological forecasts through frequency-domain fusion techniques to improve long-term forecasting accuracy. AI-gene...
🔹 Publication Date: Published on Mar 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.21768
• PDF: https://arxiv.org/pdf/2603.21768
• Github: https://github.com/Onemissed/PW-FouCast
🔹 Models citing this paper:
• https://huggingface.co/Onemiss/PW-FouCast
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Deep learning model for precipitation nowcasting that combines radar imagery with meteorological forecasts through frequency-domain fusion techniques to improve long-term forecasting accuracy. AI-gene...
🔹 Publication Date: Published on Mar 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.21768
• PDF: https://arxiv.org/pdf/2603.21768
• Github: https://github.com/Onemissed/PW-FouCast
🔹 Models citing this paper:
• https://huggingface.co/Onemiss/PW-FouCast
==================================
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❤1
✨PixelSmile: Toward Fine-Grained Facial Expression Editing
📝 Summary:
PixelSmile is a diffusion framework for fine-grained facial expression editing. It achieves better disentanglement and identity preservation through symmetric joint training and contrastive learning. This enables precise, stable, and continuous control for expression editing.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25728
• PDF: https://arxiv.org/pdf/2603.25728
• Project Page: https://ammmob.github.io/PixelSmile/
• Github: https://github.com/Ammmob/PixelSmile
🔹 Models citing this paper:
• https://huggingface.co/PixelSmile/PixelSmile
✨ Datasets citing this paper:
• https://huggingface.co/datasets/PixelSmile/FFE-Bench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Pr0f3ssi0n4ln00b/Qwen-Image-Edit-Rapid-AIO-Loras-Experimental
• https://huggingface.co/spaces/PixelSmile/PixelSmile-Demo
==================================
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#FacialExpressionEditing #DiffusionModels #AI #ComputerVision #DeepLearning
📝 Summary:
PixelSmile is a diffusion framework for fine-grained facial expression editing. It achieves better disentanglement and identity preservation through symmetric joint training and contrastive learning. This enables precise, stable, and continuous control for expression editing.
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25728
• PDF: https://arxiv.org/pdf/2603.25728
• Project Page: https://ammmob.github.io/PixelSmile/
• Github: https://github.com/Ammmob/PixelSmile
🔹 Models citing this paper:
• https://huggingface.co/PixelSmile/PixelSmile
✨ Datasets citing this paper:
• https://huggingface.co/datasets/PixelSmile/FFE-Bench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Pr0f3ssi0n4ln00b/Qwen-Image-Edit-Rapid-AIO-Loras-Experimental
• https://huggingface.co/spaces/PixelSmile/PixelSmile-Demo
==================================
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arXiv.org
PixelSmile: Toward Fine-Grained Facial Expression Editing
Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective...
❤1
✨RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models
📝 Summary:
A large-scale dataset and open-source model are developed to improve image restoration performance and close the gap with closed-source alternatives, with a dedicated benchmark for real-world degradat...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.25502
• PDF: https://arxiv.org/pdf/2603.25502
• Project Page: https://yfyang007.github.io/RealRestorer/
• Github: https://github.com/yfyang007/RealRestorer
🔹 Models citing this paper:
• https://huggingface.co/RealRestorer/RealRestorer
• https://huggingface.co/RealRestorer/RealRestorer_degradation_models
✨ Datasets citing this paper:
• https://huggingface.co/datasets/RealRestorer/RealIR-Bench
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A large-scale dataset and open-source model are developed to improve image restoration performance and close the gap with closed-source alternatives, with a dedicated benchmark for real-world degradat...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.25502
• PDF: https://arxiv.org/pdf/2603.25502
• Project Page: https://yfyang007.github.io/RealRestorer/
• Github: https://github.com/yfyang007/RealRestorer
🔹 Models citing this paper:
• https://huggingface.co/RealRestorer/RealRestorer
• https://huggingface.co/RealRestorer/RealRestorer_degradation_models
✨ Datasets citing this paper:
• https://huggingface.co/datasets/RealRestorer/RealIR-Bench
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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