Semantic Differentiation Aids Oriented Small Object Detection

Detecting small, oriented objects in remote sensing images remains a bottleneck for prevailing detection paradigms. The discriminative cues essential for detecting small instances are often inaccessible owing to the restrained spatial extent and poor visual responses, which further compromises the model and necessitates reliance on low-level patterns for identification and localization, exacerbating vulnerability to structural distortions and intra-class confusion especially in complex scenarios. To address these desiderata, we devise a Semantic Differentiation (SemDiff) framework for oriented small object detection in remote sensing images. Starting with randomly initialized category-specific units, we deliver a differentiation pipeline where distinctive features steer the evolution of these embeddings via a tailored differentiation loss. Afterwards, these class-aligned vectors function as dynamic kernels, infusing hierarchical representations with semantic understanding. Moreover, an improved centerness metric that is more accommodating to size-constrained instances is introduced. Building upon this, we design an instance-level recalibration mechanism to regulate the training process, thereby ensuring adequate optimization even for exceptionally small instances. By integrating semantic in an explicit fashion, our SemDiff efficiently facilitates the discriminative capabilities of hierarchical features, thereby revitalizing foreground responses and alleviating semantic-level ambiguity. On the challenging small object detection benchmarks SODA-A and Tiny-DOTA, our approach outstrips prevailing single-stage paradigms by a substantial margin, and achieves competitive performance to its two-stage counterparts, but with an edge of speed. Codes will be available at https://github.com/shaunyuan22/SemDiff.