Abstract In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data effectively train learning models for recognition. The attention mechanism in assists the model focus on informative segments and extract discriminative features of inputs enhance training performance. This paper investigates...

Abstract The session-based recommendation task is designed to predict the behavior of current session at next moment based on multiple anonymous sessions. Due lack user information in session, traditional model cannot be used directly interest specific users. In this paper, a hypergraph neural networks and attention mechanism (HGNNA) proposed. Firstly, features items are learned by constructing...

Recently, deep learning-based image classification and speech recognition approaches have made extensive use of attention mechanisms to achieve state-of-the-art recognition, which demonstrates the effectiveness mechanisms. Motivated by fact that frequency time information modulated radio signals are crucial for modulation this letter proposes a time-frequency mechanism convolutional neural netw...

Rock image classification is a fundamental and crucial task in the creation of geological surveys. Traditional rock methods mainly rely on manual operation, resulting high costs unstable accuracy. While existing based deep learning models have overcome limitations traditional achieved intelligent classification, they still suffer from low accuracy due to suboptimal network structures. In this s...

Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth locations actions in training videos. With only video-level supervision during training, most existing methods rely on Multiple Instance Learning (MIL) framework predict start and end frame each category video. However, MIL-based approach has major limitation capturing discriminative f...