Search Results for author:
Found 9 papers, 6 papers with code
MLPerf Mobile Inference Benchmark
This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks.
Deep Metric Learning to Rank
We propose a novel deep metric learning method by revisiting the learning to rank approach.
Hashing with Binary Matrix Pursuit
We propose theoretical and empirical improvements for two-stage hashing methods.
Hashing with Mutual Information
Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval.
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval.
MIHash: Online Hashing with Mutual Information
Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data.
Adaptive Hashing for Fast Similarity Search
With the staggering growth in image and video datasets, algorithms that provide fast similarity search and compact storage are crucial.
Online Supervised Hashing for Ever-Growing Datasets
To address these issues, we propose an online hashing method that is amenable to changes and expansions of the datasets.
Visual Word Selection without Re-Coding and Re-Pooling
Thus, given a training set for a particular computer vision task, a key problem is pruning a large codebook to select only a subset of visual words.
