The cross-media hashing retrieval generally encodes multimedia data into a common binary
hash space, which can effectively measure the correlation between samples from different …

Traditional machine learning methods have always performed the learning tasks solely. The
models of these conventional methods have to be built from scratch. The idea of domain …

Highlights•Decoupled domain adaptation model proposed for underwater fish
detection.•Enhanced feature pyramid introduced to solve the multi-scale of the fish.•Adaptors …

Despite the ecological importance of giant clams (Tridacninae), their effective management
and conservation is challenging due to their widespread distribution and labour-intensive …

In recent years, the Fisher Linear Discriminant Analysis (FLDA)-based classi cation models
are among the most successful approaches and have shown e ective performance in di …

Abstract Domain adaptation in machine learning and image processing aims to benefit from
gained knowledge of the multiple labeled training sets (ie source domain) to classify the …

Extreme learning machine (ELM) is a well-known cognitive model, that has been extended
to cross-domain tasks. Nonetheless, most existing paradigms that are based on ELM either …

Abstract Domain adaptation tackles a learning problem in a test data (target domain) by
utilizing the training data (source domain) in a related domain, often with different …

Traditional machine learning methods have always performed the learning tasks solely. The
models of these conventional methods have to be built from scratch. The idea of domain …

Image processing is a method to perform some operations on an image, in order to get an
enhanced image or to extract some useful information from it. The conventional image …