# LabelEnc: A New Intermediate Supervision Method for Object Detection

arXiv: https://arxiv.org/abs/2007.03282 · HTML: https://mlsanity.com/paper/2007.03282/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [ConQueR: Query Contrast Voxel-DETR for 3D Object Detection](https://mlsanity.com/paper/2212.07289/) (2022)
- [LGD: Label-guided Self-distillation for Object Detection](https://mlsanity.com/paper/2109.11496/) (2021)
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- [HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors](https://mlsanity.com/paper/2207.05345/) (2022)
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- [A Comprehensive Review on Deep Supervision: Theories and Applications](https://mlsanity.com/paper/2207.02376/) (2022)
- [LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection](https://mlsanity.com/paper/2407.10164/) (2024)
- [Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network](https://mlsanity.com/paper/2207.05497/) (2022)
- [Label-Guided Auxiliary Training Improves 3D Object Detector](https://mlsanity.com/paper/2207.11753/) (2022)
- [LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation](https://mlsanity.com/paper/2211.11870/) (2022)
- [KE-RCNN: Unifying Knowledge based Reasoning into Part-level Attribute Parsing](https://mlsanity.com/paper/2206.10146/) (2022)
- [StructVPR++: Distill Structural and Semantic Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2503.06601/) (2025)
- [Smooth and Stepwise Self-Distillation for Object Detection](https://mlsanity.com/paper/2303.05015/) (2023)
- [Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains](https://mlsanity.com/paper/2312.05832/) (2023)
- [Unveiling the Black Box: Independent Functional Module Evaluation for Bird's-Eye-View Perception Model](https://mlsanity.com/paper/2409.11969/) (2024)
- [SIRe-Networks: Convolutional Neural Networks Architectural Extension for Information Preservation via Skip/Residual Connections and Interlaced Auto-Encoders](https://mlsanity.com/paper/2110.02776/) (2021)

## Abstract (arXiv)

In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems. The key idea is to introduce a novel label encoding function, mapping the ground-truth labels into latent embedding, acting as an auxiliary intermediate supervision to the detection backbone during training. Our approach mainly involves a two-step training procedure. First, we optimize the label encoding function via an AutoEncoder defined in the label space, approximating the "desired" intermediate representations for the target object detector. Second, taking advantage of the learned label encoding function, we introduce a new auxiliary loss attached to the detection backbones, thus benefiting the performance of the derived detector. Experiments show our method improves a variety of detection systems by around 2% on COCO dataset, no matter one-stage or two-stage frameworks. Moreover, the auxiliary structures only exist during training, i.e. it is completely cost-free in inference time. Code is available at: https://github.com/megvii-model/LabelEnc