# Context-Guided Semantic Alignment for Feature Fusion Networks Source: [Context-Guided Semantic Alignment for Feature Fusion Networks]() Collection index: [llms.txt]() Data updated: 2026-06-16T05:00:05.515Z Authors: Hyungseop Lee, Jiho Lee, Woochul Kang Published: 2026-06-12T00:54:11.000Z ## Abstract Feature fusion networks are fundamental components in modern object detectors, aggregating multi-scale features to detect objects of varying sizes. However, directly fusing features from different pyramid levels often introduces semantic inconsistency due to their heterogeneous representations. In this paper, we propose Feature Interaction NEtwork (FINE), a lightweight semantic alignment module that refines low-level features via high-level contextual guidance using cross-level attention prior to fusion. To bridge the structural gap and ensure computational efficiency, we introduce an Alignment-Aware Token Sampling that aligns corresponding spatial regions across scales, reducing the attention complexity by an order of magnitude. The resulting attention weights generate a spatial-channel modulation map that is upsampled and applied to the low-level features via residual element-wise modulation. This mechanism ensures that the network selectively enhances semantically relevant pixels while preserving the sub-pixel localization accuracy necessary for dense prediction tasks. FINE is generally applicable to various detectors and consistently improves detection accuracy without compromising efficiency. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on VisDrone DET 2019](): Object Detection; VisDrone DET 2019; AP50: 47.2. - [Panoptic Segmentation on COCO 2017 (val)](): Panoptic Segmentation; COCO 2017 (val); PQ: 40.9. - [Semantic Segmentation on Cityscapes (val) (mIoU)](): Semantic segmentation; Cityscapes (val); mIoU: 76.4. - [Object Detection on COCO 2017 (val)](): Object Detection; COCO 2017 (val); AP: 53.3. - [Instance Segmentation on COCO 2017 (val)](): Instance Segmentation; COCO 2017 (val); APm: not recorded. ## Original sources and implementation [Original paper]() [PDF]()