TL;DR
CMFR first extracts multi-scale contextual features for coarse target localization, then combines spatial and Laplacian frequency cues to aggregate boundary and texture responses, and finally uses coarse maps as dynamic thresholds for adaptive refinement.
Small object detection in remote sensing images (RSIs) is challenging because imaging degradation weakens object textures, reduces contrast, and blurs boundaries. These effects are further aggravated by hierarchical feature extraction, where repeated downsampling weakens shallow spatial cues before they reach deeper se...
Camouflaged Object Detection (COD) aims to segment objects that blend seamlessly with their surroundings, posing significant challenges due to ambiguous boundaries and incomplete structural perception under complex backgrounds. Existing methods often rely on single-domain representations or coarse feature fusion strate...
UAV-based small object detection remains challenging because aerial targets are often densely distributed, weakly textured, partially occluded, and affected by complex backgrounds and illumination variations. To address these issues, this paper presents SAFLR-Net, a scale-aware feature enhancement and localization re...
Small objects in complex driving scenes often contain weak texture information and are highly sensitive to localization errors, which poses significant challenges to feature representation and bounding box regression. To address these issues, this paper proposes Acuity-DETR, a high-acuity visual perception model for sm...
An improved boundary optimization framework based on the BPR paradigm, termed Enhanced Boundary Patch Refinement (E-BPR), is proposed to explicitly model boundary uncertainty through adaptive mask patch refinement and demonstrates that the proposed framework consistently improves boundary quality and instance segmentat...
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