116 citations · 235 across the 19 of their papers we have counts for
7 papers · 1 filter
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…