Small object detection (SOD) constitutes a notable yet immensely arduous task, stemming from the restricted informative regions inherent in size-limited instances, which further sparks off heightened uncertainty beyond the capacity of current two-stage detectors. Specifically, the intrinsic ambiguity in small objects undermines the prevailing sampling paradigms and may mislead the model to devote futile effort to those unrecognizable targets, while the inconsistency of features utilized for the detection at two stages further exposes the hierarchical uncertainty. In this paper, we develop an Uncertainty learning framework for Small Object Detection, dubbed as Unc-SOD. By incorporating an auxiliary uncertainty branch to conventional Region Proposal Network (RPN), we model the indeterminacy at instance-level which later on serves as a surrogate criterion for sampling, thereby unearthing adequate candidates dynamically based on the varying degrees of uncertainty and facilitating the learning of proposal networks. In parallel, a Perception-and-Interaction strategy is devised to capture rich and discriminative representations, through optimizing the intrinsic properties from the regional features at the original pyramid and the assigned one, in which the perceptual process unfolds in a mutual paradigm. As the seminal attempt to model uncertainty in SOD task, our Unc-SOD yields state-of-the-art performance on two large-scale small object detection benchmarks, SODA-D and SODA-A, and the results on several SOD-oriented datasets including COCO, VisDrone, and Tsinghua-Tencent 100K also exhibit the promotion to baseline detector. This underscores the efficacy of our approach and its superiority over prevailing detectors when dealing with small instances.

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 athttps://github.com/shaunyuan22/SemDiff.