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Found 9 papers, 0 papers with code
On Binary Embedding using Circulant Matrices
To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix.
Compressing Deep Convolutional Networks using Vector Quantization
In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs.
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition.
Circulant Binary Embedding
To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix.
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications.
A Multi-View Embedding Space for Modeling Internet Images, Tags, and their Semantics
This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as image-to-image search, tag-to-image search, and image-to-tag search (image annotation).
Angular Quantization-based Binary Codes for Fast Similarity Search
Such data typically arises in a large number of vision and text applications where counts or frequencies are used as features.
Web Scale Photo Hash Clustering on A Single Machine
In addition, we propose an online clustering method based on binary k-means that is capable of clustering large photo stream on a single machine, and show applications to spam detection and trending photo discovery.
Learning Binary Codes for High-Dimensional Data Using Bilinear Projections
Recent advances in visual recognition indicate that to achieve good retrieval and classification accuracy on largescale datasets like ImageNet, extremely high-dimensional visual descriptors, e. g., Fisher Vectors, are needed.
