Structural-Semantic Reciprocal Learning for Unsupervised Visible-Infrared Person Re-Identification
Unsupervised visible-infrared person re-identification (USVI-ReID) is challenging due to the large modality gap and the lack of cross-modal identity annotations. Progressive association paradigms have been proposed to gradually bridge the gap, but they suffer from two critical bottlenecks: reliance on ambiguous global representations and unchecked propagation of pseudo-label noise in an open-loop manner. To address these issues, we propose Structural-Semantic Reciprocal Learning (SSRL), a framework that transforms open-loop association into a self-correcting closed-loop system. Structurally, w
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- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%vllm-project/semantic-router →
“Fuzzy title match (0.73): “Structural-Semantic Reciprocal Learning for Unsupervised Vis” ≈ “vllm-project/semantic-router””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Structural-Semantic Reciprocal Learning for Unsupervised Vis” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Moyao Tian →
“Structural-Semantic Reciprocal Learning for Unsupervised Visible-Infrared Person Re-Identification”
- LinkedLinked via arxiv author · 85%Shijia Liu →
“Structural-Semantic Reciprocal Learning for Unsupervised Visible-Infrared Person Re-Identification”
- LinkedLinked via arxiv author · 85%Yan Yang →
“Structural-Semantic Reciprocal Learning for Unsupervised Visible-Infrared Person Re-Identification”