69 citations · 215 across the 11 of their papers we have counts for
11 papers
V3Det: Vast Vocabulary Visual Detection Dataset
Jiaqi Wang, Pan Zhang, Tao Chu +6
Recent advances in detecting arbitrary objects in the real world are trained and evaluated on object detection datasets with a relatively restricted vocabulary. To facilitate the d…
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
Tong Wu, Jiarui Zhang, Xiao Fu +9
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, re…
Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction
Tong Wu, Jiaqi Wang, Xingang Pan +4
Neural surface reconstruction aims to reconstruct accurate 3D surfaces based on multi-view images. Previous methods based on neural volume rendering mostly train a fully implicit m…
Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
Tong Wu, Liang Pan, Junzhe Zhang +3
Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mi…
Few-Shot Object Detection via Association and DIscrimination
Yuhang Cao, Jiaqi Wang, Ying Jin +4
Object detection has achieved substantial progress in the last decade. However, detecting novel classes with only few samples remains challenging, since deep learning under low dat…
Adversarial Robustness under Long-Tailed Distribution
Tong Wu, Ziwei Liu, Qingqiu Huang +2
Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversaria…
Towards Evaluating and Training Verifiably Robust Neural Networks
Zhaoyang Lyu, Minghao Guo, Tong Wu +3
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP train…
Shaping Deep Feature Space towards Gaussian Mixture for Visual Classification
Weitao Wan, Jiansheng Chen, Cheng Yu +3
The softmax cross-entropy loss function has been widely used to train deep models for various tasks. In this work, we propose a Gaussian mixture (GM) loss function for deep neural…
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator
Tong Wu, Xuefei Ning, Wenshuo Li +3
In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on ve…
Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
Tong Wu, Qingqiu Huang, Ziwei Liu +2
We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions. Compared to conventional sin…
Adversarial Robustness of Deep Sensor Fusion Models
Shaojie Wang, Tong Wu, Ayan Chakrabarti +1
We experimentally study the robustness of deep camera-LiDAR fusion architectures for 2D object detection in autonomous driving. First, we find that the fusion model is usually both…