106 citations · 133 across the 6 of their papers we have counts for
6 papers
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…
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…
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
Jiaxin Cheng, Xiao Liang, Xingjian Shi +3
Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts. In this paper, we propose LayoutDiffuse that adapts a foundational d…
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Tianjun Xiao, Yichong Xu, Kuiyuan Yang +3
Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the p…
Scale-Invariant Convolutional Neural Networks
Yichong Xu, Tianjun Xiao, Jiaxing Zhang +2
Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited. The popul…