85 citations · 258 across the 28 of their papers we have counts for
29 papers
MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection
Junkai Xu, Liang Peng, Haoran Cheng +5
In the field of monocular 3D detection, it is common practice to utilize scene geometric clues to enhance the detector's performance. However, many existing works adopt these clues…
Learning Occupancy for Monocular 3D Object Detection
Liang Peng, Junkai Xu, Haoran Cheng +6
Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to fac…
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning
Yang Liu, Shen Yan, Yuge Zhang +5
Vision Transformers have shown great performance in single tasks such as classification and segmentation. However, real-world problems are not isolated, which calls for vision tran…
APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
Hengjia Li, Tu Zheng, Zhihao Chi +5
Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to dir…
General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment
Liang Xie, Yibo Yang, Wenxiao Wang +4
Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformati…
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…
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Yixuan Su, Lei Shu, Elman Mansimov +4
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascade…
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
Wenxiao Wang, Lu Yao, Long Chen +4
Transformers have made great progress in dealing with computer vision tasks. However, existing vision transformers do not yet possess the ability of building the interactions among…
Salient Object Ranking with Position-Preserved Attention
Hao Fang, Daoxin Zhang, Yi Zhang +5
Instance segmentation can detect where the objects are in an image, but hard to understand the relationship between them. We pay attention to a typical relationship, relative salie…
Attacking Adversarial Attacks as A Defense
Boxi Wu, Heng Pan, Li Shen +6
It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, fai…
Discriminative-Generative Dual Memory Video Anomaly Detection
Xin Guo, Zhongming Jin, Chong Chen +5
Recently, people tried to use a few anomalies for video anomaly detection (VAD) instead of only normal data during the training process. A side effect of data imbalance occurs when…
OCM3D: Object-Centric Monocular 3D Object Detection
Liang Peng, Fei Liu, Senbo Yan +2
Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection. However, methods based on them have shortcomings of either not well capturing the s…
Lidar Point Cloud Guided Monocular 3D Object Detection
Liang Peng, Fei Liu, Zhengxu Yu +5
Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box label…