1 code implementation • ICCV 2023 • Gengwei Zhang, Liyuan Wang, Guoliang Kang, Ling Chen, Yunchao Wei
The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data.
1 code implementation • 5 Dec 2022 • Siyu Jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Humphrey Shi
Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results.
1 code implementation • CVPR 2022 • BinBin Yang, Xinchi Deng, Han Shi, Changlin Li, Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, Xiaodan Liang
To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes.
no code implementations • ICCV 2021 • Hang Xu, Ning Kang, Gengwei Zhang, Chuanlong Xie, Xiaodan Liang, Zhenguo Li
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
2 code implementations • NeurIPS 2021 • Gengwei Zhang, Guoliang Kang, Yi Yang, Yunchao Wei
Directly performing cross-attention may aggregate these features from support to query and bias the query features.
Ranked #53 on Few-Shot Semantic Segmentation on PASCAL-5i (1-Shot)
1 code implementation • ICLR 2021 • Peidong Liu, Gengwei Zhang, Bochao Wang, Hang Xu, Xiaodan Liang, Yong Jiang, Zhenguo Li
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
no code implementations • 1 Jan 2021 • Hang Xu, Ning Kang, Gengwei Zhang, Xiaodan Liang, Zhenguo Li
The resulting model zoo is more training efficient than SOTA NAS models, e. g. 6x faster than RegNetY-16GF, and 1. 7x faster than EfficientNetB3.
no code implementations • 7 Dec 2020 • Gengwei Zhang, Yiming Gao, Hang Xu, Hao Zhang, Zhenguo Li, Xiaodan Liang
Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention.
Ranked #17 on Panoptic Segmentation on COCO test-dev
2 code implementations • NeurIPS 2020 • Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin
In this work, we propose an efficient, cooperative and highly automated framework to simultaneously search for all main components including backbone, segmentation branches, and feature fusion module in a unified panoptic segmentation pipeline based on the prevailing one-shot Network Architecture Search (NAS) paradigm.
no code implementations • CVPR 2020 • Yangxin Wu, Gengwei Zhang, Yiming Gao, Xiajun Deng, Ke Gong, Xiaodan Liang, Liang Lin
We introduce a Bidirectional Graph Reasoning Network (BGRNet), which incorporates graph structure into the conventional panoptic segmentation network to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes.