Search Results - "YUILLE, Alan"

Improving Transferability of Adversarial Examples With Input Diversity by Xie, Cihang, Zhang, Zhishuai, Zhou, Yuyin, Bai, Song, Wang, Jianyu, Ren, Zhou, Yuille, Alan L.

“…Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples --- crafted by adding…”
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Non-Rigid Point Set Registration by Preserving Global and Local Structures by Jiayi Ma, Ji Zhao, Yuille, Alan L.

“…In previous work on point registration, the input point sets are often represented using Gaussian mixture models and the registration is then addressed through…”
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Deep Nets: What have They Ever Done for Vision? by Yuille, Alan L., Liu, Chenxi

“…This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial…”
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Feature Denoising for Improving Adversarial Robustness by Xie, Cihang, Wu, Yuxin, Maaten, Laurens van der, Yuille, Alan L., He, Kaiming

“…Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests…”
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Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples by Yuan Gao, Jiayi Ma, Yuille, Alan L.

“…This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize…”
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs by Liang-Chieh Chen, Papandreou, George, Kokkinos, Iasonas, Murphy, Kevin, Yuille, Alan L.

“…In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have…”
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Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation by Liu, Chenxi, Chen, Liang-Chieh, Schroff, Florian, Adam, Hartwig, Hua, Wei, Yuille, Alan L., Fei-Fei, Li

“…Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image…”
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Grouped Spatial-Temporal Aggregation for Efficient Action Recognition by Luo, Chenxu, Yuille, Alan

“…Temporal reasoning is an important aspect of video analysis. 3D CNN shows good performance by exploring spatial-temporal features jointly in an unconstrained…”
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Robust Point Matching via Vector Field Consensus by Ma, Jiayi, Zhao, Ji, Tian, Jinwen, Yuille, Alan L., Tu, Zhuowen

“…In this paper, we propose an efficient algorithm, called vector field consensus, for establishing robust point correspondences between two sets of points. Our…”
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Abdominal multi-organ segmentation with organ-attention networks and statistical fusion by Wang, Yan, Zhou, Yuyin, Shen, Wei, Park, Seyoun, Fishman, Elliot K., Yuille, Alan L.

“…•A novel deep network framework for accurate multi-organ segmentation on abdominal CT.•Organ-attention network to enhance discriminative information from…”
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Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion by Kortylewski, Adam, He, Ju, Liu, Qing, Yuille, Alan L.

“…Recent work has shown that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional…”
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NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction by Gao, Yuan, Ma, Jiayi, Zhao, Mingbo, Liu, Wei, Yuille, Alan L.

“…In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature…”
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Few-Shot Image Recognition by Predicting Parameters from Activations by Qiao, Siyuan, Liu, Chenxi, Shen, Wei, Yuille, Alan

“…In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large…”
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Vision as Bayesian inference: analysis by synthesis? by Yuille, Alan, Kersten, Daniel

“…We argue that the study of human vision should be aimed at determining how humans perform natural tasks with natural images. Attempts to understand the…”
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CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning by Wei, Chen, Sohn, Kihyuk, Mellina, Clayton, Yuille, Alan, Yang, Fan

“…Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods…”
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Snapshot Distillation: Teacher-Student Optimization in One Generation by Yang, Chenglin, Xie, Lingxi, Su, Chi, Yuille, Alan L.

“…Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. Teacher-student…”
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Universal Physical Camouflage Attacks on Object Detectors by Huang, Lifeng, Gao, Chengying, Zhou, Yuyin, Xie, Cihang, Yuille, Alan L., Zou, Changqing, Liu, Ning

“…In this paper, we study physical adversarial attacks on object detectors in the wild. Previous works mostly craft instance-dependent perturbations only for…”
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Adversarial Examples Improve Image Recognition by Xie, Cihang, Tan, Mingxing, Gong, Boqing, Wang, Jiang, Yuille, Alan L., Le, Quoc V.

“…Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image…”
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Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation by Zhou, Yuyin, Li, Zhe, Bai, Song, Chen, Xinlei, Han, Mei, Wang, Chong, Fishman, Elliot, Yuille, Alan

“…Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires…”
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OOD-CV-v2 : An Extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images by Zhao, Bingchen, Wang, Jiahao, Ma, Wufei, Jesslen, Artur, Yang, Siwei, Yu, Shaozuo, Zendel, Oliver, Theobalt, Christian, Yuille, Alan L., Kortylewski, Adam

“…Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they…”
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