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Found 7 papers, 0 papers with code
Compression of Deep Neural Networks for Image Instance Retrieval
One major drawback of CNN-based {\it global descriptors} is that uncompressed deep neural network models require hundreds of megabytes of storage making them inconvenient to deploy in mobile applications or in custom hardware.
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
The first one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a mathematical theory for computing group invariant transformations with feed-forward neural networks.
Group Invariant Deep Representations for Image Instance Retrieval
Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated.
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing
Then, triplet networks, a rank learning scheme based on weight sharing nets is used to fine-tune the binary embedding functions to retain as much as possible of the useful metric properties of the original space.
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval
The second part of the study focuses on the impact of geometrical transformations such as rotations and scale changes.
Co-Regularized Deep Representations for Video Summarization
A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites.
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval.
