AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization
Contrastive Language-Image Pretraining learns a shared representation space through large-scale contrastive learning. However, existing methods that enforce global consistency regularization overlook a key challenge: the inherent information asymmetry between images and text: captions typically describe only one specific aspect of an image, thus images with similar visual content can be paired with completely divergent textual content and semantic information. Consequently, global regularizers inadvertently impose constraints between visually similar images whose captions describe divergent as
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%HKUDS/LightRAG →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”
- LinkedLinked via arxiv author · 85%Yiyang Yao →
“AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization”
- LinkedLinked via arxiv author · 85%Shanglin Liu →
“AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization”
- LinkedLinked via arxiv author · 85%Jianming Lv →
“AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization”