Multi-Label Image Retrieval
4 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Label Image Retrieval
Latest papers with no code
Instance-weighted Central Similarity for Multi-label Image Retrieval
Deep hashing has been widely applied to large-scale image retrieval by encoding high-dimensional data points into binary codes for efficient retrieval.
Online Hashing with Similarity Learning
In the proposed framework, the hash functions are fixed and a parametric similarity function for the binary codes is learnt online to adapt to the streaming data.
BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval
In our experiments, we show the potential of BigEarthNet-MM for multi-modal multi-label image retrieval and classification problems by considering several state-of-the-art DL models.
Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval
Finally, we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture.
Instance-Aware Hashing for Multi-Label Image Retrieval
The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category.
Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval
Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels.
