no code implementations • 4 Apr 2024 • Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, Shubham Tulsiani
We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images.
1 code implementation • 17 Jun 2022 • Hanzhe Hu, Yinbo Chen, Jiarui Xu, Shubhankar Borse, Hong Cai, Fatih Porikli, Xiaolong Wang
As such, IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
1 code implementation • NeurIPS 2021 • Hanzhe Hu, Fangyun Wei, Han Hu, Qiwei Ye, Jinshi Cui, LiWei Wang
The confidence bank is leveraged as an indicator to tilt training towards under-performing categories, instantiated in three strategies: 1) adaptive Copy-Paste and CutMix data augmentation approaches which give more chance for under-performing categories to be copied or cut; 2) an adaptive data sampling approach to encourage pixels from under-performing category to be sampled; 3) a simple yet effective re-weighting method to alleviate the training noise raised by pseudo-labeling.
1 code implementation • CVPR 2021 • Hanzhe Hu, Shuai Bai, Aoxue Li, Jinshi Cui, LiWei Wang
In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem.
1 code implementation • ICCV 2021 • Hanzhe Hu, Jinshi Cui, LiWei Wang
Inspired by recent progress in unsupervised contrastive learning, we propose the region-aware contrastive learning (RegionContrast) for semantic segmentation in the supervised manner.
no code implementations • 8 Dec 2020 • Deyi Ji, Haoran Wang, Hanzhe Hu, Weihao Gan, Wei Wu, Junjie Yan
Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks.
no code implementations • ECCV 2020 • Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, Junjie Yan
Specifically, the CDGC module takes the coarse segmentation result as class mask to extract node features for graph construction and performs dynamic graph convolutions on the constructed graph to learn the feature aggregation and weight allocation.