[PDF][PDF] Multi-head attention graph network for few shot learning
The majority of existing graph-network-based few-shot models focus on a node-similarity
update mode. The lack of adequate information intensifies the risk of overtraining. In this …
update mode. The lack of adequate information intensifies the risk of overtraining. In this …
Unsupervised Deep Hashing With Deep Semantic Distillation
Many existing unsupervised hashing methods attempt to preserve as much semantic
information as possible by reconstructing the input data. However, this approach can result …
information as possible by reconstructing the input data. However, this approach can result …
Deep Unsupervised Hashing with Semantic Consistency Learning
Hashing method has attracted more attention in recent years because of its low storage
consumption and high retrieval performance. Most unsupervised hashing methods first …
consumption and high retrieval performance. Most unsupervised hashing methods first …
Improve Unsupervised Deep Hashing Via Masked Contrastive Learning
Unsupervised hashing method aims to generate compact binary hash codes for images
without label supervision. Existing unsupervised hashing methods usually learn binary hash …
without label supervision. Existing unsupervised hashing methods usually learn binary hash …
Deep supervised hashing by classification for image retrieval
Hashing has been widely used to approximate the nearest neighbor search for image
retrieval due to its high computation efficiency and low storage requirement. With the …
retrieval due to its high computation efficiency and low storage requirement. With the …