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Unsupervised Representation Learning by Predicting Image Rotations
Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features.Expand
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Dynamic Few-Shot Visual Learning Without Forgetting
The human visual system has the remarkably ability to be able to effortlessly learn novel concepts from only a few examples. Mimicking the same behavior on machine learning vision systems is anExpand
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Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-basedExpand
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PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab modelExpand
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Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
The problem of computing category agnostic bounding box proposals is utilized as a core component in many computer vision tasks and thus has lately attracted a lot of attention. In this work weExpand
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Detect, Replace, Refine: Deep Structured Prediction for Pixel Wise Labeling
Pixel wise image labeling is an interesting and challenging problem with great significance in the computer vision community. In order for a dense labeling algorithm to be able to achieve accurateExpand
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LocNet: Improving Localization Accuracy for Object Detection
We propose a novel object localization methodology with the purpose of boosting the localization accuracy of stateof-the-art object detection systems. Our model, given a search region, aims atExpand
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Boosting Few-Shot Visual Learning With Self-Supervision
Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods andExpand
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Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning
Given an initial recognition model already trained on a set of base classes, the goal of this work is to develop a meta-model for few-shot learning. The meta-model, given as input some novel classesExpand
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Learning Representations by Predicting Bags of Visual Words
Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose aExpand
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