repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · yesterday

CVHub520/X-AnyLabeling

X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.

Lineage graph

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) · 49%Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials →
PossiblePossibly related (embedding) · 48%AdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting →
PossiblePossibly related (embedding) · 46%TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling →
PossiblePossibly related (embedding) · 46%Low-cost concept-based localized explanations: How far can we get with training-free approaches? →
FuzzySimilar title/name (fuzzy) · 87%Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR →
“Fuzzy title match (0.94): “Progressive Refinement: An Iterative Pseudo-Labeling Approac” ≈ “CVHub520/X-AnyLabeling””
FuzzySimilar title/name (fuzzy) · 87%HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures →
“Fuzzy title match (0.94): “HERMES: A Multi-Granularity Labeling Substrate for Pre-train” ≈ “CVHub520/X-AnyLabeling””
FuzzySimilar title/name (fuzzy) · 87%InstanceControl: Controllable Complex Image Generation without Instance Labeling →
“Fuzzy title match (0.94): “InstanceControl: Controllable Complex Image Generation witho” ≈ “CVHub520/X-AnyLabeling””
PossiblePossibly related (embedding) · 26%Vision as Unified Multimodal Generation →
“Possibly related via embedding similarity 0.57 (not asserted). Timestamp check: artifact slightly before paper (-3d).”

Implements

paperBeyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials paperAdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting paperTRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling paperLow-cost concept-based localized explanations: How far can we get with training-free approaches?paperProgressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR paperHERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures paperInstanceControl: Controllable Complex Image Generation without Instance Labeling

Related to (incoming)

paperVision as Unified Multimodal Generation paperWyvern: An Agentic Framework for Generating Grounded Multimodal Reports

Related across the graph

paperProgressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR paperWyvern: An Agentic Framework for Generating Grounded Multimodal Reports paperHERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures paperBeyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials paperInstanceControl: Controllable Complex Image Generation without Instance Labeling paperAdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting paperTRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling paperLow-cost concept-based localized explanations: How far can we get with training-free approaches?paperVision as Unified Multimodal Generation

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