577 citations · 904 across the 15 of their papers we have counts for
14 papers · 1 filter
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
Guoqiang Jin, Fan Yang, Mingshan Sun +7
Self-supervised pre-training and transformer-based networks have significantly improved the performance of object detection. However, most of the current self-supervised object det…
Explore the Power of Synthetic Data on Few-shot Object Detection
Shaobo Lin, Kun Wang, Xingyu Zeng +1
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. The few training samples restrict the performance o…
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Shaobo Lin, Kun Wang, Xingyu Zeng +1
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. However, detecting novel categories with only a few…
Explore the Power of Dropout on Few-shot Learning
Shaobo Lin, Xingyu Zeng, Rui Zhao
The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this pa…
A Unified Framework with Meta-dropout for Few-shot Learning
Shaobo Lin, Xingyu Zeng, Rui Zhao
Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations. In this paper, we utilize the idea of meta-learning to…
Rethinking Pseudo-LiDAR Representation
Xinzhu Ma, Shinan Liu, Zhiyi Xia +3
The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the…
Adapting Object Detectors with Conditional Domain Normalization
Peng Su, Kun Wang, Xingyu Zeng +4
Real-world object detectors are often challenged by the domain gaps between different datasets. In this work, we present the Conditional Domain Normalization (CDN) to bridge the do…
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation
Yingjie Cai, Buyu Li, Zeyu Jiao +3
Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images. Since the location recovery in 3D space is quite difficult on acc…
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
Buyu Li, Wanli Ouyang, Lu Sheng +2
We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D informa…
Crafting GBD-Net for Object Detection
Xingyu Zeng, Wanli Ouyang, Junjie Yan +9
The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. Effective integration of loca…
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Kai Kang, Hongsheng Li, Junjie Yan +8
The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as Goo…
Window-Object Relationship Guided Representation Learning for Generic Object Detections
Xingyu Zeng, Wanli Ouyang, Xiaogang Wang
In existing works that learn representation for object detection, the relationship between a candidate window and the ground truth bounding box of an object is simplified by thresh…
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
Wanli Ouyang, Xiaogang Wang, Xingyu Zeng +8
In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple…
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
Wanli Ouyang, Ping Luo, Xingyu Zeng +12
In this paper, we propose multi-stage and deformable deep convolutional neural networks for object detection. This new deep learning object detection diagram has innovations in mul…