Search Results - "Xie, Lingxi"
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1
Genetic CNN
Published in 2017 IEEE International Conference on Computer Vision (ICCV) (01-10-2017)“…The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as…”
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Conference Proceeding -
2
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
Published in International journal of computer vision (01-03-2021)“…With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks. Like the…”
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Journal Article -
3
Conformer: Local Features Coupling Global Representations for Visual Recognition
Published in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (01-01-2021)“…Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global…”
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4
Adversarial Examples for Semantic Segmentation and Object Detection
Published in 2017 IEEE International Conference on Computer Vision (ICCV) (01-10-2017)“…It has been well demonstrated that adversarial examples, i.e., natural images with visually imperceptible perturbations added, cause deep networks to fail on…”
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5
Spatial Pooling of Heterogeneous Features for Image Classification
Published in IEEE transactions on image processing (01-05-2014)“…In image classification tasks, one of the most successful algorithms is the bag-of-features (BoFs) model. Although the BoF model has many advantages, such as…”
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6
DisturbLabel: Regularizing CNN on the Loss Layer
Published in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (01-06-2016)“…During a long period of time we are combating overfitting in the CNN training process with model regularization, including weight decay, model averaging, data…”
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7
Hierarchical Part Matching for Fine-Grained Visual Categorization
Published in 2013 IEEE International Conference on Computer Vision (01-12-2013)“…As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing attention these years. Different with traditional…”
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8
Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation
Published in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (01-06-2018)“…We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep…”
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9
Fine-Grained Image Search
Published in IEEE transactions on multimedia (01-05-2015)“…Large-scale image search has been attracting lots of attention from both academic and commercial fields. The conventional bag-of-visual-words (BoVW) model with…”
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10
SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images
Published in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (01-06-2019)“…In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named…”
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11
Network Adjustment: Channel and Block Search Guided by Resource Utilization Ratio
Published in International journal of computer vision (01-03-2022)“…It is an important problem to design resource-efficient neural architectures. One solution is adjusting the number of channels in each layer and the number of…”
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12
Simple Techniques Make Sense: Feature Pooling and Normalization for Image Classification
Published in IEEE transactions on circuits and systems for video technology (01-07-2016)“…Image classification is a fundamental task in computer vision, implying a wide range of challenging problems, such as object recognition, scene understanding,…”
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13
VoxSeP: semi-positive voxels assist self-supervised 3D medical segmentation
Published in Multimedia systems (01-02-2023)“…Medical image segmentation enjoys the advantage of understanding 3D contexts, but 3D networks are prone to over-fitting due to the limited amount of annotated…”
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14
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection Under Partial Occlusion
Published in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (01-06-2018)“…In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should…”
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15
Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience
Published in Journal of the American College of Radiology (01-09-2019)Get full text
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16
InterActive: Inter-Layer Activeness Propagation
Published in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (01-06-2016)“…An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural…”
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17
SORT: Second-Order Response Transform for Visual Recognition
Published in 2017 IEEE International Conference on Computer Vision (ICCV) (01-10-2017)“…In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks. We propose a novel approach named…”
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18
Orientational Pyramid Matching for Recognizing Indoor Scenes
Published in 2014 IEEE Conference on Computer Vision and Pattern Recognition (01-06-2014)“…Scene recognition is a basic task towards image understanding. Spatial Pyramid Matching (SPM) has been shown to be an efficient solution for spatial context…”
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19
RIDE: Reversal Invariant Descriptor Enhancement
Published in 2015 IEEE International Conference on Computer Vision (ICCV) (01-12-2015)“…In many fine-grained object recognition datasets, image orientation (left/right) might vary from sample to sample. Since handcrafted descriptors such as SIFT…”
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20
Feature normalization for part-based image classification
Published in 2013 IEEE International Conference on Image Processing (01-09-2013)“…Part-based Bag-of-Features (BoF) models such as Spatial Pyramid Matching (SPM) play an important role in image classification. Before sending the feature…”
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Conference Proceeding