85 citations · 258 across the 28 of their papers we have counts for
12 papers · 1 filter
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection
Chenxi Huang, Tong He, Haidong Ren +3
Compared to typical multi-sensor systems, monocular 3D object detection has attracted much attention due to its simple configuration. However, there is still a significant gap betw…
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations
Minghao Chen, Renbo Tu, Chenxi Huang +3
Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly…
One-shot Implicit Animatable Avatars with Model-based Priors
Yangyi Huang, Hongwei Yi, Weiyang Liu +6
Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large…
Boosting Semi-Supervised 3D Object Detection with Semi-Sampling
Xiaopei Wu, Yang Zhao, Liang Peng +6
Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D obj…
Towards In-distribution Compatibility in Out-of-distribution Detection
Boxi Wu, Jie Jiang, Haidong Ren +7
Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. T…
Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph
Honghui Yang, Zili Liu, Xiaopei Wu +4
Two-stage detectors have gained much popularity in 3D object detection. Most two-stage 3D detectors utilize grid points, voxel grids, or sampled keypoints for RoI feature extractio…
DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
Liang Peng, Xiaopei Wu, Zheng Yang +2
Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. T…
Towards Efficient Adversarial Training on Vision Transformers
Boxi Wu, Jindong Gu, Zhifeng Li +3
Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention. Recent work showed that ViTs are also vulnerable to adversar…
Neural Collapse Inspired Attraction-Repulsion-Balanced Loss for Imbalanced Learning
Liang Xie, Yibo Yang, Deng Cai +1
Class imbalance distribution widely exists in real-world engineering. However, the mainstream optimization algorithms that seek to minimize error will trap the deep learning model…
CLRNet: Cross Layer Refinement Network for Lane Detection
Tu Zheng, Yifei Huang, Yang Liu +4
Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern…
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Liang Peng, Senbo Yan, Boxi Wu +3
Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection meth…
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion
Xiaopei Wu, Liang Peng, Honghui Yang +5
Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different represen…