794 citations · 4.4k across the 64 of their papers we have counts for
19 papers · 1 filter
Learning Depth-Guided Convolutions for Monocular 3D Object Detection
Mingyu Ding, Yuqi Huo, Hongwei Yi +4
3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task…
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
Shaoshuai Shi, Chaoxu Guo, Li Jiang +4
We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our proposed method deep…
Side-Aware Boundary Localization for More Precise Object Detection
Jiaqi Wang, Wenwei Zhang, Yuhang Cao +6
Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regre…
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow
Mingyu Ding, Zhe Wang, Bolei Zhou +3
A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised…
Dense Fusion Classmate Network for Land Cover Classification
Chao Tian, Cong Li, Jianping Shi
Recently, FCNs based methods have made great progress in semantic segmentation. Different with ordinary scenes, satellite image owns specific characteristics, which elements always…
Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints
Yan Xu, Xinge Zhu, Jianping Shi +3
Depth completion aims to recover dense depth maps from sparse depth measurements. It is of increasing importance for autonomous driving and draws increasing attention from the visi…
Robust Multi-Modality Multi-Object Tracking
Wenwei Zhang, Hui Zhou, Shuyang Sun +3
Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential moveme…
Graph-guided Architecture Search for Real-time Semantic Segmentation
Peiwen Lin, Peng Sun, Guangliang Cheng +3
Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited int…
GDRQ: Group-based Distribution Reshaping for Quantization
Haibao Yu, Tuopu Wen, Guangliang Cheng +3
Low-bit quantization is challenging to maintain high performance with limited model capacity (e.g., 4-bit for both weights and activations). Naturally, the distribution of both wei…
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Peng Sun, Peiwen Lin, Guangliang Cheng +3
Video object segmentation aims at accurately segmenting the target object regions across consecutive frames. It is technically challenging for coping with complicated factors (e.g.…
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
Shaoshuai Shi, Zhe Wang, Jianping Shi +2
3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work Poi…
MMDetection: Open MMLab Detection Toolbox and Benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang +22
We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The to…
Towards Instance-level Image-to-Image Translation
Zhiqiang Shen, Mingyang Huang, Jianping Shi +2
Unpaired Image-to-image Translation is a new rising and challenging vision problem that aims to learn a mapping between unaligned image pairs in diverse domains. Recent advances in…
Libra R-CNN: Towards Balanced Learning for Object Detection
Jiangmiao Pang, Kai Chen, Jianping Shi +3
Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work…
Switchable Whitening for Deep Representation Learning
Xingang Pan, Xiaohang Zhan, Jianping Shi +2
Normalization methods are essential components in convolutional neural networks (CNNs). They either standardize or whiten data using statistics estimated in predefined sets of pixe…
R-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images
Jiangmiao Pang, Cong Li, Jianping Shi +2
Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still rema…
A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
Kui Xu, Zhe Wang, Jiangping Shi +2
Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Me…
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
Shuyang Sun, Jiangmiao Pang, Jianping Shi +2
The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e.g., image-level, region-level, and pixel-level are div…
Hybrid Task Cascade for Instance Segmentation
Kai Chen, Jiangmiao Pang, Jiaqi Wang +9
Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A…