RAIL-BENCH is the first standardized benchmark suite for railway perception with five challenges, real-world datasets, and a novel LineAP metric for rail track detection.
Yolo- world: Real-time open-vocabulary object detection
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RoboAtlas integrates frontier exploration, global semantic maps, and VLM reasoning via contextual multi-armed bandit to achieve 90.6% success rate on GOAT-Bench Val Unseen and 100% task success in real 1800m2 environments.
COMPASS is a manipulation-aware active sensing framework that raises simulated manipulation success rates by 24.25% over information-gain-only baselines in a new four-level confined-space benchmark.
UrbanClipAtlas integrates RAG, taxonomy-aware extraction, and video grounding into a chat interface for retrieving and interpreting events in long urban videos from street intersections.
citing papers explorer
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Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain
RAIL-BENCH is the first standardized benchmark suite for railway perception with five challenges, real-world datasets, and a novel LineAP metric for rail track detection.
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RoboAtlas: Contextual Active SLAM
RoboAtlas integrates frontier exploration, global semantic maps, and VLM reasoning via contextual multi-armed bandit to achieve 90.6% success rate on GOAT-Bench Val Unseen and 100% task success in real 1800m2 environments.
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COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy
COMPASS is a manipulation-aware active sensing framework that raises simulated manipulation success rates by 24.25% over information-gain-only baselines in a new four-level confined-space benchmark.
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UrbanClipAtlas: A Visual Analytics Framework for Event and Scene Retrieval in Urban Videos
UrbanClipAtlas integrates RAG, taxonomy-aware extraction, and video grounding into a chat interface for retrieving and interpreting events in long urban videos from street intersections.