116 citations · 235 across the 19 of their papers we have counts for
20 papers
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection
Tong He, Pei Sun, Zhaoqi Leng +3
We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds. Our main motivation is fusing object-aware latent embeddings i…
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Ke Fan, Jingshi Lei, Xuelin Qian +5
Video amodal segmentation is a particularly challenging task in computer vision, which requires to deduce the full shape of an object from the visible parts of it. Recently, some s…
Object-Centric Multiple Object Tracking
Zixu Zhao, Jiaze Wang, Max Horn +13
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing th…
Unsupervised Open-Vocabulary Object Localization in Videos
Ke Fan, Zechen Bai, Tianjun Xiao +11
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video obje…
Coarse-to-Fine Amodal Segmentation with Shape Prior
Jianxiong Gao, Xuelin Qian, Yikai Wang +4
Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to…
Learning for Transductive Threshold Calibration in Open-World Recognition
Qin Zhang, Dongsheng An, Tianjun Xiao +6
In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true n…
Learning Manifold Dimensions with Conditional Variational Autoencoders
Yijia Zheng, Tong He, Yixuan Qiu +1
Although the variational autoencoder (VAE) and its conditional extension (CVAE) are capable of state-of-the-art results across multiple domains, their precise behavior is still not…
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…
GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds
Honghui Yang, Tong He, Jiaheng Liu +5
Despite the tremendous progress of Masked Autoencoders (MAE) in developing vision tasks such as image and video, exploring MAE in large-scale 3D point clouds remains challenging du…
3D-QueryIS: A Query-based Framework for 3D Instance Segmentation
Jiaheng Liu, Tong He, Honghui Yang +6
Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness. Besides, inevitable variations of…
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Zhaoqi Leng, Guowang Li, Chenxi Liu +5
Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly…
ARCH++: Animation-Ready Clothed Human Reconstruction Revisited
Tong He, Yuanlu Xu, Shunsuke Saito +2
We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both the v…
Learning Hierarchical Graph Neural Networks for Image Clustering
Yifan Xing, Tong He, Tianjun Xiao +6
We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated…
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction
Tong He, John Collomosse, Hailin Jin +1
We propose Geo-PIFu, a method to recover a 3D mesh from a monocular color image of a clothed person. Our method is based on a deep implicit function-based representation to learn l…
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition
Jialin Gao, Tong He, Xi Zhou +1
A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependenci…
SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning
Albert Zhao, Tong He, Yitao Liang +3
We describe a policy learning approach to map visual inputs to driving controls conditioned on turning command that leverages side tasks on semantics and object affordances via a l…
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
Tong He, Stefano Soatto
We present a method to infer 3D pose and shape of vehicles from a single image. To tackle this ill-posed problem, we optimize two-scale projection consistency between the generated…
GeoNet: Deep Geodesic Networks for Point Cloud Analysis
Tong He, Haibin Huang, Li Yi +4
Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling. However, such connectivity information is lost in point clouds. Thus we in…
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
Yang Wang, Haibin Huang, Chuan Wang +3
Graphics Interchange Format (GIF) is a highly portable graphics format that is ubiquitous on the Internet. Despite their small sizes, GIF images often contain undesirable visual ar…
FashionNet: Personalized Outfit Recommendation with Deep Neural Network
Tong He, Yang Hu
With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great need. We design algorithms which automatically sugg…