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Changyong Shu

4 accepted papers

2026

FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection

AAAI 2026technical

Camera-based multi-view 3D detection is crucial for autonomous driving. PETR and its variants (PETRs) excel in benchmarks but face deployment challenges due to high computational cost and memory footprint. Quantization is an effective technique for compressing deep neural networks by reducing the bi

Cited by 0SourcePDFScholar
2023

3DPPE: 3D Point Positional Encoding for Transformer-based Multi-Camera 3D Object Detection

ICCV 2023poster

Transformer-based methods have swept the benchmarks on 2D and 3D detection on images. Because tokenization before the attention mechanism drops the spatial information, positional encoding becomes critical for those methods. Recent works found that encodings based on samples of the 3D viewing rays c…

Cited by 25PDFcodeScholar
2021

Channel-Wise Knowledge Distillation for Dense Prediction

ICCV 2021poster

Knowledge distillation (KD) has been proven a simple and effective tool for training compact dense prediction models. Lightweight student networks are trained by extra supervision transferred from large teacher networks. Most previous KD variants for dense prediction tasks align the activation maps…

Cited by 366PDFcodeScholar