no code implementations • 30 Mar 2023 • Lucas Beyer, Bo Wan, Gagan Madan, Filip Pavetic, Andreas Steiner, Alexander Kolesnikov, André Susano Pinto, Emanuele Bugliarello, Xiao Wang, Qihang Yu, Liang-Chieh Chen, Xiaohua Zhai
A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well.
no code implementations • 28 Jan 2023 • Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, Alan Yuille, Zaiyi Liu, Ling Zhang
A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan.
2 code implementations • 4 Oct 2022 • Chenglin Yang, Siyuan Qiao, Qihang Yu, Xiaoding Yuan, Yukun Zhu, Alan Yuille, Hartwig Adam, Liang-Chieh Chen
The tiny-MOAT family is also benchmarked on downstream tasks, serving as a baseline for the community.
Ranked #28 on
Image Classification
on ImageNet
(using extra training data)
1 code implementation • 8 Jul 2022 • Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
However, we observe that most existing transformer-based vision models simply borrow the idea from NLP, neglecting the crucial difference between languages and images, particularly the extremely large sequence length of spatially flattened pixel features.
Ranked #2 on
Panoptic Segmentation
on COCO test-dev
no code implementations • CVPR 2022 • Qihang Yu, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering.
Ranked #6 on
Panoptic Segmentation
on COCO test-dev
no code implementations • CVPR 2022 • Dahun Kim, Jun Xie, Huiyu Wang, Siyuan Qiao, Qihang Yu, Hong-Seok Kim, Hartwig Adam, In So Kweon, Liang-Chieh Chen
We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner.
1 code implementation • 2 Dec 2021 • Ju He, Shuo Yang, Shaokang Yang, Adam Kortylewski, Xiaoding Yuan, Jie-Neng Chen, Shuai Liu, Cheng Yang, Qihang Yu, Alan Yuille
To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations.
1 code implementation • 17 Jun 2021 • Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.
1 code implementation • NeurIPS 2021 • Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan Yuille, Wei Shen
It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context.
14 code implementations • 8 Feb 2021 • Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L. Yuille, Yuyin Zhou
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Ranked #3 on
Medical Image Segmentation
on ACDC
1 code implementation • CVPR 2021 • Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, Alan Yuille
We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance.
no code implementations • 25 Nov 2020 • Yutong Bai, Haoqi Fan, Ishan Misra, Ganesh Venkatesh, Yongyi Lu, Yuyin Zhou, Qihang Yu, Vikas Chandra, Alan Yuille
To this end, we present Temporal-aware Contrastive self-supervised learningTaCo, as a general paradigm to enhance video CSL.
1 code implementation • ICLR 2021 • Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie
To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.
Ranked #522 on
Image Classification
on ImageNet
2 code implementations • CVPR 2020 • Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan Yuille
However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and 2) it is an open problem to discover an optimal configuration to embed NL blocks into mobile neural networks.
Ranked #59 on
Neural Architecture Search
on ImageNet
1 code implementation • 28 Mar 2020 • Qihang Yu, Yingwei Li, Jieru Mei, Yuyin Zhou, Alan L. Yuille
3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition.
no code implementations • 18 Mar 2020 • Yingda Xia, Qihang Yu, Wei Shen, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population.
no code implementations • 19 Feb 2020 • Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, Daguang Xu
In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph.
no code implementations • CVPR 2020 • Qihang Yu, Dong Yang, Holger Roth, Yutong Bai, Yixiao Zhang, Alan L. Yuille, Daguang Xu
3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts.
no code implementations • 2 Apr 2019 • Qihang Yu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille
With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method's effectiveness and ability in capturing 3D information.
2 code implementations • CVPR 2018 • Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille
The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.
Ranked #1 on
Pancreas Segmentation
on TCIA Pancreas-CT Dataset