19 citations · 58 across the 8 of their papers we have counts for
8 papers
UniMD: Towards Unifying Moment Retrieval and Temporal Action Detection
Yingsen Zeng, Yujie Zhong, Chengjian Feng +1
Temporal Action Detection (TAD) focuses on detecting pre-defined actions, while Moment Retrieval (MR) aims to identify the events described by open-ended natural language within un…
InstaGen: Enhancing Object Detection by Training on Synthetic Dataset
Chengjian Feng, Yujie Zhong, Zequn Jie +2
In this paper, we present a novel paradigm to enhance the ability of object detector, e.g., expanding categories or improving detection performance, by training on synthetic datase…
FastPillars: A Deployment-friendly Pillar-based 3D Detector
Sifan Zhou, Zhi Tian, Xiangxiang Chu +7
The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios. Existing BEV-based (i.e., Bird Eye View) detectors favor sparse convolution…
AeDet: Azimuth-invariant Multi-view 3D Object Detection
Chengjian Feng, Zequn Jie, Yujie Zhong +2
Recent LSS-based multi-view 3D object detection has made tremendous progress, by processing the features in Brid-Eye-View (BEV) via the convolutional detector. However, the typical…
PromptDet: Towards Open-vocabulary Detection using Uncurated Images
Chengjian Feng, Yujie Zhong, Zequn Jie +5
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations. To achieve that, we make…
TOOD: Task-aligned One-stage Object Detection
Chengjian Feng, Yujie Zhong, Yu Gao +2
One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to…
Exploring Classification Equilibrium in Long-Tailed Object Detection
Chengjian Feng, Yujie Zhong, Weilin Huang
The conventional detectors tend to make imbalanced classification and suffer performance drop, when the distribution of the training data is severely skewed. In this paper, we prop…
Domain Confusion with Self Ensembling for Unsupervised Adaptation
Jiawei Wang, Zhaoshui He, Chengjian Feng +4
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem. A common approach for this problem is to transfer knowledge from a relate…