RemoteAgent uses RL fine-tuning on VagueEO to align MLLMs for vague EO intent recognition, handling simple tasks internally and routing dense predictions to tools via Model Context Protocol.
Dota: A large-scale dataset for object detection in aerial images
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 2verdicts
UNVERDICTED 2roles
dataset 1polarities
use dataset 1representative citing papers
Hausdorff distance-based matching and adaptive query denoising improve Rotated DETR, yielding +4.18 to +4.99 AP50 gains on DOTA-v2.0, DOTA-v1.5, and DIOR-R with ResNet-50.
citing papers explorer
-
RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs
RemoteAgent uses RL fine-tuning on VagueEO to align MLLMs for vague EO intent recognition, handling simple tasks internally and routing dense predictions to tools via Model Context Protocol.
-
Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
Hausdorff distance-based matching and adaptive query denoising improve Rotated DETR, yielding +4.18 to +4.99 AP50 gains on DOTA-v2.0, DOTA-v1.5, and DIOR-R with ResNet-50.