15 citations · 34 across the 6 of their papers we have counts for
6 papers
detrex: Benchmarking Detection Transformers
Tianhe Ren, Shilong Liu, Feng Li +13
The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and…
A Strong and Reproducible Object Detector with Only Public Datasets
Tianhe Ren, Jianwei Yang, Shilong Liu +6
This work presents Focal-Stable-DINO, a strong and reproducible object detection model which achieves 64.6 AP on COCO val2017 and 64.8 AP on COCO test-dev using only 700M parameter…
Geometric-aware Pretraining for Vision-centric 3D Object Detection
Linyan Huang, Huijie Wang, Jia Zeng +4
Multi-camera 3D object detection for autonomous driving is a challenging problem that has garnered notable attention from both academia and industry. An obstacle encountered in vis…
3D Data Augmentation for Driving Scenes on Camera
Wenwen Tong, Jiangwei Xie, Tianyu Li +7
Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone. While data augmentation is an effective technique to enrich…
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Feng Li, Ailing Zeng, Shilong Liu +4
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance. Its success cannot be achieved without the re-introduction of multi-scale feature fusion in t…
Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking
Peng Gao, Renrui Zhang, Rongyao Fang +4
Masked Autoencoders (MAE) have been popular paradigms for large-scale vision representation pre-training. However, MAE solely reconstructs the low-level RGB signals after the decod…