CiF is a large new civil infrastructure segmentation dataset that shows zero-shot foundation models and domain-supervised models plateau at roughly 25% mAP, establishing infrastructure inspection as an open challenge for current visual AI.
Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick
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FaSTA* combines LLM fast planning with A* search and inductive subroutine mining to create an efficient agent for multi-turn image editing tasks.
GAF creates 4D dynamic scene models by adding motion to 3D Gaussians, enabling better reconstruction and 7.3% higher success in robotic tasks.
CORP is introduced as the first public benchmark dataset for multi-modal roadside perception tasks in campus scenarios, collected from diverse pole-mounted sensors with rich annotations beyond standard bounding boxes.
citing papers explorer
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Cracks in the Foundation: A Civil Infrastructure Dataset to Challenge Vision Foundation Models
CiF is a large new civil infrastructure segmentation dataset that shows zero-shot foundation models and domain-supervised models plateau at roughly 25% mAP, establishing infrastructure inspection as an open challenge for current visual AI.
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FaSTA$^*$: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing
FaSTA* combines LLM fast planning with A* search and inductive subroutine mining to create an efficient agent for multi-turn image editing tasks.
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GAF: Gaussian Action Field as a 4D Representation for Dynamic World Modeling in Robotic Manipulation
GAF creates 4D dynamic scene models by adding motion to 3D Gaussians, enabling better reconstruction and 7.3% higher success in robotic tasks.
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CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks
CORP is introduced as the first public benchmark dataset for multi-modal roadside perception tasks in campus scenarios, collected from diverse pole-mounted sensors with rich annotations beyond standard bounding boxes.