Search Results for author: Zhiding Yu

Found 43 papers, 24 papers with code

UFO²: A Unified Framework towards Omni-supervised Object Detection

1 code implementation ECCV 2020 Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Alexander G. Schwing, Jan Kautz

Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.

Object Detection

Learning Contrastive Representation for Semantic Correspondence

no code implementations22 Sep 2021 Taihong Xiao, Sifei Liu, Shalini De Mello, Zhiding Yu, Jan Kautz, Ming-Hsuan Yang

Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even for objects from the same category, and 2) labeling pixel-level dense correspondences is labor intensive and infeasible to scale.

Contrastive Learning Semantic correspondence

Panoptic SegFormer

no code implementations8 Sep 2021 Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Tong Lu, Ping Luo

We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers.

Panoptic Segmentation

Towards Reducing Labeling Cost in Deep Object Detection

no code implementations22 Jun 2021 Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixe, Jose M. Alvarez

Deep neural networks have reached very high accuracy on object detection but their success hinges on large amounts of labeled data.

Active Learning Object Detection

SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

no code implementations17 Jun 2021 Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar

A student network then learns to mimic the expert policy by supervised learning with strong augmentations, making its representation more robust against visual variations compared to the expert.

Autonomous Driving Image Augmentation +1

Practical Machine Learning Safety: A Survey and Primer

no code implementations9 Jun 2021 Sina Mohseni, Haotao Wang, Zhiding Yu, Chaowei Xiao, Zhangyang Wang, Jay Yadawa

The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations.

Autonomous Vehicles Domain Adaptation

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

5 code implementations31 May 2021 Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders.

Semantic Segmentation

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

1 code implementation12 Apr 2021 Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez

Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection.

Transferable Unsupervised Robust Representation Learning

no code implementations1 Jan 2021 De-An Huang, Zhiding Yu, Anima Anandkumar

We upend this view and show that URRL improves both the natural accuracy of unsupervised representation learning and its robustness to corruptions and adversarial noise.

Data Augmentation Transfer Learning +1

UFO$^2$: A Unified Framework towards Omni-supervised Object Detection

no code implementations21 Oct 2020 Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Alexander G. Schwing, Jan Kautz

Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.

Object Detection

Deep Distributionally Robust Learning for Calibrated Uncertainties under Domain Shift

no code implementations8 Oct 2020 Haoxuan Wang, Anqi Liu, Zhiding Yu, Junchi Yan, Yisong Yue, Anima Anandkumar

The framework is demonstrated to generate calibrated uncertainties that benefit many downstream tasks, including unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) where methods such as self-training and FixMatch use uncertainties to select confident pseudo-labels.

Density Ratio Estimation Unsupervised Domain Adaptation

Delving Deeper into Anti-aliasing in ConvNets

2 code implementations21 Aug 2020 Xueyan Zou, Fanyi Xiao, Zhiding Yu, Yong Jae Lee

Aliasing refers to the phenomenon that high frequency signals degenerate into completely different ones after sampling.

Instance Segmentation Semantic Segmentation

Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

1 code implementation ECCV 2020 Yang Zou, Xiaodong Yang, Zhiding Yu, B. V. K. Vijaya Kumar, Jan Kautz

To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively.

Person Re-Identification Unsupervised Domain Adaptation

Unsupervised Controllable Generation with Self-Training

no code implementations17 Jul 2020 Grigorios G. Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar

Instead, we propose an unsupervised framework to learn a distribution of latent codes that control the generator through self-training.

Neural Networks with Recurrent Generative Feedback

1 code implementation NeurIPS 2020 Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Y. Tsao, Anima Anandkumar

This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment.

Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter

no code implementations14 Jul 2020 Guilin Liu, Rohan Taori, Ting-Chun Wang, Zhiding Yu, Shiqiu Liu, Fitsum A. Reda, Karan Sapra, Andrew Tao, Bryan Catanzaro

Specifically, we directly treat the whole encoded feature map of the input texture as transposed convolution filters and the features' self-similarity map, which captures the auto-correlation information, as input to the transposed convolution.

Texture Synthesis

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

no code implementations28 Jun 2020 Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.

Pancreas Segmentation Unsupervised Domain Adaptation +1

Confidence Regularized Self-Training

2 code implementations ICCV 2019 Yang Zou, Zhiding Yu, Xiaofeng Liu, B. V. K. Vijaya Kumar, Jinsong Wang

Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.

Image Classification Semantic Segmentation +1

Regularizing Neural Networks via Minimizing Hyperspherical Energy

1 code implementation CVPR 2020 Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M. Rehg, Li Xiong, Le Song

Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.

Partial Convolution based Padding

3 code implementations28 Nov 2018 Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro

In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks.

General Classification Semantic Segmentation

Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training

1 code implementation18 Oct 2018 Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, Jinsong Wang

In this paper, we propose a novel UDA framework based on an iterative self-training procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels.

Semantic Segmentation Synthetic-to-Real Translation +1

Simultaneous Edge Alignment and Learning

3 code implementations ECCV 2018 Zhiding Yu, Weiyang Liu, Yang Zou, Chen Feng, Srikumar Ramalingam, B. V. K. Vijaya Kumar, Jan Kautz

Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.

Edge Detection Representation Learning

Learning towards Minimum Hyperspherical Energy

4 code implementations NeurIPS 2018 Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.

Decoupled Networks

1 code implementation CVPR 2018 Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song

Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.

Learning Strict Identity Mappings in Deep Residual Networks

1 code implementation CVPR 2018 Xin Yu, Zhiding Yu, Srikumar Ramalingam

A family of super deep networks, referred to as residual networks or ResNet, achieved record-beating performance in various visual tasks such as image recognition, object detection, and semantic segmentation.

Object Detection Semantic Segmentation

Deep Hyperspherical Learning

no code implementations NeurIPS 2017 Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song

In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.

Representation Learning

CASENet: Deep Category-Aware Semantic Edge Detection

11 code implementations CVPR 2017 Zhiding Yu, Chen Feng, Ming-Yu Liu, Srikumar Ramalingam

To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.

Edge Detection Object Proposal Generation +1

SphereFace: Deep Hypersphere Embedding for Face Recognition

12 code implementations CVPR 2017 Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.

Face Identification Face Recognition +1

Large-Margin Softmax Loss for Convolutional Neural Networks

2 code implementations7 Dec 2016 Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang

Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs).

General Classification

Structured Hough Voting for Vision-based Highway Border Detection

no code implementations18 Nov 2014 Zhiding Yu, Wende Zhang, B. V. K. Vijaya Kumar, Dan Levi

We propose a vision-based highway border detection algorithm using structured Hough voting.

Multi-Task Regularization with Covariance Dictionary for Linear Classifiers

no code implementations21 Oct 2013 Fanyi Xiao, Ruikun Luo, Zhiding Yu

In this paper we propose a multi-task linear classifier learning problem called D-SVM (Dictionary SVM).

Transfer Learning

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

no code implementations5 Sep 2012 Xi Peng, Zhiding Yu, Huajin Tang, Zhang Yi

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i. e., intra-subspace data points).

Image Clustering Motion Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.