Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost

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High-dimensional signature compression for large-scale image classification

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Efficient additive kernels via explicit feature maps

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Learning Visual Similarity Measures for Comparing Never Seen Objects

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Local Features and Kernels for Classication of Texture and Object Categories: A Comprehensive Study

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The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories

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ImageNet classification with deep convolutional neural networks

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ImageNet Large Scale Visual Recognition Challenge

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iCaRL: Incremental Classifier and Representation Learning

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DeViSE: A Deep Visual-Semantic Embedding Model

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Towards Open Set Deep Networks

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Deep Metric Learning via Lifted Structured Feature Embedding

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Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly

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Image Classification with the Fisher Vector: Theory and Practice

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A Closer Look at Few-shot ClassificationPreprint

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Dynamic Few-Shot Visual Learning Without Forgetting

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Semantic Autoencoder for Zero-Shot Learning

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Zero-Shot Learning by Convex Combination of Semantic EmbeddingsPreprint

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Learning to Compare: Relation Network for Few-Shot Learning

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Learning Deep Structure-Preserving Image-Text Embeddings

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Label-Embedding for Image Classification

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Learning a Deep Embedding Model for Zero-Shot Learning

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Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs

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Label-Embedding for Attribute-Based Classification

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Zero-Shot Learning via Semantic Similarity Embedding

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An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

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A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics

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Learning Two-Branch Neural Networks for Image-Text Matching Tasks

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The limits and potentials of deep learning for robotics

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A Survey on Metric Learning for Feature Vectors and Structured DataPreprint

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Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost

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Rethinking Knowledge Graph Propagation for Zero-Shot Learning

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Zero-Shot Learning via Joint Latent Similarity Embedding

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The Extreme Value Machine

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Zero Shot Recognition with Unreliable AttributesPreprint

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Cloud-based robot grasping with the google object recognition engine

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Zero-shot object recognition by semantic manifold distance

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Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classiffication

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Learning Graph Embeddings for Compositional Zero-shot Learning

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From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

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Gradient Matching Generative Networks for Zero-Shot Learning

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A Meta-Learning Perspective on Cold-Start Recommendations for Items

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PARN: Position-Aware Relation Networks for Few-Shot Learning

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Low-Shot Learning with Large-Scale Diffusion

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On Learning the Geodesic Path for Incremental Learning

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Learning and the Unknown: Surveying Steps toward Open World Recognition

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Kernel Null Space Methods for Novelty Detection

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Learning Discriminative Latent Attributes for Zero-Shot Classification

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Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation

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Zero Shot Detection

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Label Embedding: A Frugal Baseline for Text Recognition

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A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning

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Learning Graph Embeddings for Open World Compositional Zero-Shot Learning

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How should we evaluate supervised hashing?

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Deep graph similarity learning: a survey

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Crossover Learning for Fast Online Video Instance Segmentation

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Computer Vision – ECCV 2012

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Semi-Supervised Zero-Shot Classification with Label Representation Learning

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SCAN: Scattering Characteristics Analysis Network for Few-Shot Aircraft Classification in High-Resolution SAR Images

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Zero-Shot Learning on Semantic Class Prototype Graph

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Triple Verification Network for Generalized Zero-Shot Learning

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Zero-Shot Recognition via Structured Prediction

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Toward Training Recurrent Neural Networks for Lifelong Learning

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Personalized Classifier for Food Image Recognition

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Quadruplet-Wise Image Similarity Learning

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Few-Shot Learning With a Strong Teacher

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GILE: A Generalized Input-Label Embedding for Text Classification

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Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection

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P-ODN: Prototype-based Open Deep Network for Open Set Recognition

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Dynamic Few-Shot Visual Learning without ForgettingPreprint

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Robust Few-Shot Learning for User-Provided Data

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Video Face Clustering With Unknown Number of Clusters

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Metric Learning

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ODN: Opening the Deep Network for Open-Set Action Recognition

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Unclonable Photonic Crystal Hydrogels with Controllable Encoding Capacity for Anticounterfeiting

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Revisiting Distillation and Incremental Classifier Learning

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Cognitively-Inspired Model for Incremental Learning Using a Few Examples

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Bayesian Neighborhood Component Analysis

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Large-Scale Visual Font Recognition

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Structural color‐based physical unclonable function

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Parameter Free Large Margin Nearest Neighbor for Distance Metric Learning

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Robust Distance Metric Learning via Bayesian Inference

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Generative Mixup Networks for Zero-Shot Learning

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Few-Shot Class Incremental Learning Leveraging Self-Supervised Features

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Meta-Learning for Beam Prediction in a Dual-Band Communication System

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Periocular Recognition Using Unsupervised Convolutional RBM Feature Learning

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Few-Shot Learning via Feature Hallucination with Variational Inference

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Mental Retrieval of Remote Sensing Images via Adversarial Sketch-Image Feature Learning

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Learning Visual Emotion Representations From Web Data

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OpenGAN: Open-Set Recognition via Open Data Generation

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The limits and potentials of deep learning for robotics

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Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition

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M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental LearningPreprint

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Distance Metric Learning via Iterated Support Vector Machines

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Pseudo Transfer with Marginalized Corrupted Attribute for Zero-shot Learning

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A Strategy for an Uncompromising Incremental Learner

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Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

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Feature Adaptive Co-Segmentation by Complexity Awareness

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Learning a Distance Metric from Relative Comparisons between Quadruplets of Images

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Gaussian Visual-Linguistic Embedding for Zero-Shot Recognition

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Extreme value theory for anomaly detection – the GPD classifier

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Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost

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Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

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Metric learning applied for automatic large scale image classification

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Zero-Shot Image Classification Based on a Learnable Deep Metric

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