Heads fitted on frozen features, one per (task, backbone). Evaluate without training. Each head is valid only against its named backbone.
🤝 Open to Collab
Turhan Can Kargın
turhancan97
P(doom) <0.01%
AI & ML interests
Computer Vision, Deep Learning, Machine Learning, Robotics, Reinforcement Learning, Control Theory
Recent Activity
updated a model about 1 hour ago
turhancan97/wildmatch-checkpoints authored a paper 1 day ago
WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification new activity 1 day ago
turhancan97/wildmatch-checkpoints:Add pipeline tagOrganizations
Spatial relation probing, given an image and an object triplet, it predicts whether the target is Left / Right / Between relative to reference objects
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turhancan97/SpaRRTa-probes
Image Classification • Updated • 1 -
SpaRRTA
🧭1Classify object direction (Front/Back/Left/Right) in images
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turhancan97/SpaRRTa
Viewer • Updated • 149k • 162 • 1 -
SpaRRTa: A Synthetic Benchmark for Evaluating Spatial Intelligence in Visual Foundation Models
Paper • 2601.11729 • Published • 1
Heads fitted on frozen features, one per (task, backbone). Evaluate without training. Each head is valid only against its named backbone.
Spatial relation probing, given an image and an object triplet, it predicts whether the target is Left / Right / Between relative to reference objects
-
turhancan97/SpaRRTa-probes
Image Classification • Updated • 1 -
SpaRRTA
🧭1Classify object direction (Front/Back/Left/Right) in images
-
turhancan97/SpaRRTa
Viewer • Updated • 149k • 162 • 1 -
SpaRRTa: A Synthetic Benchmark for Evaluating Spatial Intelligence in Visual Foundation Models
Paper • 2601.11729 • Published • 1