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Found 14 papers, 2 papers with code
Interferences in match kernels
The second one involves equalising the match of a single descriptor to the aggregated vector.
Polysemous codes
This paper considers the problem of approximate nearest neighbor search in the compressed domain.
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach
Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision.
LEWIS: Latent Embeddings for Word Images and their Semantics
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images.
Fisher Vectors Meet Neural Networks: A Hybrid Classification Architecture
Fisher Vectors (FV) and Convolutional Neural Networks(CNN) are two image classification pipelines with different strengths.
Deep Fishing: Gradient Features from Deep Nets
Convolutional Networks (ConvNets) have recently improved image recognition performance thanks to end-to-end learning of deep feed-forward models from raw pixels.
Understanding the Fisher Vector: a multimodal part model
Fisher Vectors and related orderless visual statistics have demonstrated excellent performance in object detection, sometimes superior to established approaches such as the Deformable Part Models.
Label-Embedding for Image Classification
Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce.
Ranked #7 on
Multi-label zero-shot learning
on Open Images V4
Discovering beautiful attributes for aesthetic image analysis
We then describe how these three key components of AVA - images, scores, and comments - can be effectively leveraged to learn visual attributes.
What makes an Image Iconic? A Fine-Grained Case Study
A natural approach to teaching a visual concept, e. g. a bird species, is to show relevant images.
Generalized Max Pooling
Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions.
Transformation Pursuit for Image Classification
We propose a principled algorithm Image Transformation Pursuit (ITP) for the automatic selection of a compact set of transformations.
Label-Embedding for Attribute-Based Classification
The label embedding framework offers other advantages such as the ability to leverage alternative sources of information in addition to attributes (e. g. class hierarchies) or to transition smoothly from zero-shot learning to learning with large quantities of data.
