arXiv AI
DRA-SAM: Disagreement-Driven Referential Active SAM for Low-Altitude Drone Referring Segmentation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
2d ago
High-fidelity training-free photovoltaic module mapping from multi-view UAV imagery
The paper introduces MV-PVMapper, a training‑free framework that combines vision foundation models (SAM 3) with UAV photogrammetry to produce high‑fidelity maps of photovoltaic modules. By generating candidate contours, correcting for surface slope, and iteratively refining segmentation across multiple views, the method recovers 7,710 of 7,711 modules with no false positives and achieves macro‑averaged precision and recall above 98 %. The approach outperforms orthomosaic segmentation and multi‑view pixel‑voting baselines, reaching a Jaccard index of 97.55 % without any task‑specific training data.
arXiv AI
Sep 10
Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots. The method also employs object‑conditioned visual reasoning and egocentric regional guidance, achieving state‑of‑the‑art success rates on the UAV‑ON benchmark.
arXiv Computer Vision
5d ago
Beyond Trial-and-Error: A Geospatial-Guided Active Reasoning Framework for Robust Aerial Vision-and-Language Navigation
The paper introduces GeoReasoner, a framework for Aerial Vision-and-Language Navigation that replaces reactive trial-and-error with geospatial-guided active reasoning. It first uses pre-search geospatial reasoning to generate region-level priors and limit the goal hypothesis space, then performs hypothesis-driven exploration to gather discriminative evidence, and finally applies consistency-aware goal verification to avoid premature confirmations. Experiments on the CityNav benchmark show GeoReasoner achieves state‑of‑the‑art zero‑shot performance, rivaling supervised methods.
arXiv AI
Jul 29
Filling Before Advancing: Capability-Gap-Driven Post-Training for Scenario-Specialized Remote Sensing MLLMs
arXiv:2607. 22205v2 Announce Type: replace-cross Abstract: Remote sensing multimodal large language models (RS-MLLMs) have improved general aerial-image understanding.
arXiv AI
Jul 14
VehAnchor: Metadata-Free Metric Scale Recovery from Vehicle Cues in Aerial Imagery
arXiv:2603. 04277v2 Announce Type: replace-cross Abstract: Autonomous aerial robots operating in GPS-denied or communication-degraded environments frequently lose access to camera metadata and telemetry, leaving onboard perception systems unable to recover the absolute metric scale of the scene.
Hugging Face Trending Papers
Sep 8
Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots, while also employing object‑conditioned visual reasoning and temporally stable regional guidance. Experiments on the UAV‑ON benchmark show AeroBelief outperforms prior methods in success rate, object success rate, and SPL.