5 accepted papers
Due to the dynamic changes in flight motion and viewpoint, the objects in unmanned aerial vehicle (UAV) tracking scenarios often suffer from drastic appearance variations. Existing UAV trackers often leverage a frame-level matching mechanism, which measures the appearance similarity between the obje…
Existing leading deep learning-based Co-saliency Detection (CoD) methods often learn the consensus features from the input image group without considering the complexity of each image. Despite the demonstrated success, the input images may contain hard samples with high complexity, e.g., those conta…
Existing leading remote sensing change detection (RSCD) often takes a semantic-agnostic learning paradigm, which uses a binary ground-truth mask as supervision for model training. Despite the demonstrated success, due to the intrinsic characteristic of extremely complicated scene changes in RS image…
Existing change detection (CD) methods often directly fuse the multi-level features from bi-temporal remote sensing images without discriminatively considering each pixel's importance. Despite the demonstrated success, unselectively mixing the features degrades the model's performance to effectively…
Existing deep learning based remote sensing change detection (RSCD) methods only rely on binary ground-truth to guide the network learning while neglecting the useful semantic guidance. As a result, the network can be readily misled by irrelevant category changes, leading to degraded performance and…