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iDETR: Implicit DETR for Tiny Object Detection

NeurIPS 2026 · Detection and segmentation › CNN features · approach: Region-based and query-based detectors

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Proposes iDETR, an implicit DETR-based detector for tiny object detection that avoids discrete multi-scale feature pyramids to address limited spatial detail and quantization errors.

Problem

Tiny object detection suffers from extremely limited spatial detail; existing multi-scale feature pyramid methods have insufficient spatial resolution, quantization errors, poor localization, and heavy compute and memory cost.

Approach

Replaces discrete multi-scale feature maps with an implicit (continuous) feature representation within a DETR framework to improve localization of tiny objects while reducing computational and memory overhead. The abstract is truncated, so specifics beyond this are not stated.

Experimental setup

Builds on: DETR

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