iDETR: Implicit DETR for Tiny Object Detection

Tiny object detection (TOD) remains challenging due to the extremely limited spatial details of small objects. Existing methods heavily rely on multi-scale feature pyramids with discrete feature maps, which suffer from insufficient spatial resolution, quantization errors, and poor localization accuracy, while imposing heavy computational and memory overhead. We present the implicit DEtection TRansformer (iDETR), an efficient detector that operates exclusively on low-resolution single-scale features. It incorporates two novel components: (1) implicit Attention (iAttn), which leverages implicit neural representations to model continuous features and enables precise sub-pixel querying beyond discrete grid limitations; and (2) Centroid-Guided Query Initialization (CGQI) for robust query initialization under single-scale constraints. We further propose head-conditional sampling in iAttn, which reduces querying computational cost by 4× and memory footprint by 3× without sacrificing performance. Extensive experiments demonstrate the effectiveness of iDETR. Especially, on AI-TODv2, iDETR outperforms state-of-the-art methods by 0.2% AP overall, with particularly strong gains of 1.1% AP on very tiny objects, while reducing computational cost by 37%. The code is available at https://anonymous.4open.science/r/iDETR-FC25/.