# Beyond Task-Driven Features for Object Detection Source: [Beyond Task-Driven Features for Object Detection]() Collection index: [llms.txt]() Data updated: 2026-04-08T05:00:04.658Z Authors: Meilun Zhou, Alina Zare Published: 2026-04-04T19:34:13.000Z ## Abstract Task-driven features learned by modern object detectors optimize end task loss yet often capture shortcut correlations that fail to reflect underlying annotation structure. Such representations limit transfer, interpretability, and robustness when task definitions change or supervision becomes sparse. This paper introduces an annotation-guided feature augmentation framework that injects embeddings into an object detection backbone. The method constructs dense spatial feature grids from annotation-guided latent spaces and fuses them with feature pyramid representations to influence region proposal and detection heads. Experiments across wildlife and remote sensing datasets evaluate classification, localization, and data efficiency under multiple supervision regimes. Results show consistent improvements in object focus, reduced background sensitivity, and stronger generalization to unseen or weakly supervised tasks. The findings demonstrate that aligning features with annotation geometry yields more meaningful representations than purely task optimized features. ## 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 AWIR 20% (test)](): Object Detection; AWIR 20% (test); mAP50: 69. ## Original sources and implementation [Original paper]() [PDF]()