Different Instances, Different Supervision: Instance-Adaptive Point-Supervised Oriented Object Detection

Point annotations reduce labeling costs for oriented object detection but provide neither object scale nor orientation. Complementary mask-based supervision sources provide extent cues, yet their relative reliability varies with the local spatial configuration of each instance. A patch-wise source choice can therefore be unsuitable for individual instances when isolated and densely clustered objects coexist in remote-sensing imagery. We find that local spatial configuration provides an effective cue for instance-level supervision selection, yet spatial cues alone cannot fully determine which source is most suitable for each instance. Based on this observation, we propose InstaPoint, an instance-adaptive framework that combines spatially guided initialization with prediction-guided supervision assignment. Instance Density Calibration (IDC) provides prediction-independent instance-specific initial assignments, while Instance-wise Supervision Assignment (ISA) subsequently selects, for each instance, the candidate supervision most consistent with the detector’s current prediction and retains the selected source for the remainder of training. ISA revises the initial supervision source for 47.62% of instances on DIOR-R and 42.99% on DOTA-v1.0, indicating substantial instance-wise reassignment beyond the spatial initialization. InstaPoint changes how extent supervision is assigned while retaining the baseline detector architecture and other training objectives. Under the end-to-end and two-stage pseudo-labeling protocols, InstaPoint achieves 42.49/47.10 $A{P}_{50}$ on DIOR-R and 62.53/67.74 $A{P}_{50}$ on DOTA-v1.0, surpassing the strongest point-supervised baseline by 0.79/0.50 points on DIOR-R and 2.92/1.65 points on DOTA-v1.0.