repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 11h ago
voxel51/fiftyone
Refine high-quality datasets and visual AI models
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 57%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- PossiblePossibly related (embedding) · 52%Ink3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative Models →
- PossiblePossibly related (embedding) · 51%No Place to Hide: Benchmarking Video Hallucination with Background-Controlled Pairs →
- PossiblePossibly related (embedding) · 50%Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models →
- PossiblePossibly related (embedding) · 47%DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing →
- PossiblePossibly related (embedding) · 49%Optimizing Visual Generative Models via Distribution-wise Rewards →
- PossiblePossibly related (embedding) · 47%NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity →
- PossiblePossibly related (embedding) · 46%By modeling visual saliency, AI improves ratings of artistic product designs - Tech Xplore →
Covers
Implements
paperInk3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative ModelspaperNo Place to Hide: Benchmarking Video Hallucination with Background-Controlled PairspaperPreserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
Implements (incoming)
paperDisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and EditingpaperOptimizing Visual Generative Models via Distribution-wise RewardspaperNEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual SelectivitypaperObject-centric LeJEPApaperHoloCount: A Holistic Visual Counting Benchmark for MLLMspaperVisual graphs for image classification: does the structure affect performance?paperDo Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image DetectionpaperSigLIP-HD by Fine-to-Coarse Supervision
Covers (incoming)
Related to (incoming)
Related across the graph
paperHoloCount: A Holistic Visual Counting Benchmark for MLLMspaperVisual graphs for image classification: does the structure affect performance?newsBy modeling visual saliency, AI improves ratings of artistic product designs - Tech XplorepaperNo Place to Hide: Benchmarking Video Hallucination with Background-Controlled PairspaperDisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and EditingpaperObject-centric LeJEPAnewsInto the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-TuningpaperMetric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AIpaperOptimizing Visual Generative Models via Distribution-wise RewardspaperNEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual SelectivitypaperPreserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion ModelspaperDo Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image DetectionpaperSigLIP-HD by Fine-to-Coarse SupervisionpaperInk3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative ModelsnewsThinking and rethinking data AI readinesspaperDataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection