Search Results for author: Siyuan Qiao

Found 22 papers, 12 papers with code

DeepLab2: A TensorFlow Library for Deep Labeling

1 code implementation17 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.

ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation

1 code implementation CVPR 2021 Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public.

Monocular Depth Estimation Panoptic Segmentation

Batch Normalization with Enhanced Linear Transformation

1 code implementation28 Nov 2020 Yuhui Xu, Lingxi Xie, Cihang Xie, Jieru Mei, Siyuan Qiao, Wei Shen, Hongkai Xiong, Alan Yuille

Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions.

Scaling Wide Residual Networks for Panoptic Segmentation

no code implementations23 Nov 2020 Liang-Chieh Chen, Huiyu Wang, Siyuan Qiao

The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant of the Residual Networks (ResNets) by stacking a small number of residual blocks with large channel sizes, have demonstrated outstanding performance on multiple dense prediction tasks.

 Ranked #1 on Panoptic Segmentation on Cityscapes val (using extra training data)

Instance Segmentation Panoptic Segmentation

Deeply Shape-guided Cascade for Instance Segmentation

1 code implementation CVPR 2021 Hao Ding, Siyuan Qiao, Alan Yuille, Wei Shen

The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across multiple stages.

Instance Segmentation Region Proposal +1

Rethinking Normalization and Elimination Singularity in Neural Networks

1 code implementation21 Nov 2019 Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille

To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models.

Image Classification Instance Segmentation +2

TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion

no code implementations9 Sep 2019 Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, Alan Yuille

Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.

General Classification Object Classification +1

Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization

1 code implementation CVPR 2019 Siyuan Qiao, Zhe Lin, Jianming Zhang, Alan Yuille

By simply replacing standard optimizers with Neural Rejuvenation, we are able to improve the performances of neural networks by a very large margin while using similar training efforts and maintaining their original resource usages.

Network Pruning Neural Architecture Search

Generalized Coarse-to-Fine Visual Recognition with Progressive Training

no code implementations29 Nov 2018 Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, Qi Tian, Elliot K. Fishman, Alan L. Yuille

Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn.

Curriculum Learning Image Classification +2

Robust Face Detection via Learning Small Faces on Hard Images

1 code implementation28 Nov 2018 Zhishuai Zhang, Wei Shen, Siyuan Qiao, Yan Wang, Bo wang, Alan Yuille

In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images.

Face Detection

Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

no code implementations ECCV 2018 Yan Wang, Lingxi Xie, Siyuan Qiao, Ya zhang, Wenjun Zhang, Alan L. Yuille

Convolution is spatially-symmetric, i. e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recognition.

Classification General Classification +1

Deep Co-Training for Semi-Supervised Image Recognition

1 code implementation ECCV 2018 Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo wang, Alan Yuille

We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework.

Unleashing the Potential of CNNs for Interpretable Few-Shot Learning

no code implementations ICLR 2018 Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille

Our models are based on the idea of encoding objects in terms of visual concepts, which are interpretable visual cues represented by the feature vectors within CNNs.

Few-Shot Learning

Single-Shot Object Detection with Enriched Semantics

no code implementations CVPR 2018 Zhishuai Zhang, Siyuan Qiao, Cihang Xie, Wei Shen, Bo wang, Alan L. Yuille

Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.

Object Detection Semantic Segmentation

Gradually Updated Neural Networks for Large-Scale Image Recognition

no code implementations ICML 2018 Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo wang, Alan Yuille

Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner.

Few-shot Learning by Exploiting Visual Concepts within CNNs

no code implementations22 Nov 2017 Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille

In this work, we address these limitations of CNNs by developing novel, flexible, and interpretable models for few-shot learning.

Few-Shot Learning

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

no code implementations ICCV 2017 Siyuan Qiao, Wei Shen, Weichao Qiu, Chenxi Liu, Alan Yuille

We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.

Object Proposal Generation

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