Titlebook: Image and Graphics; 12th International C Huchuan Lu,Wanli Ouyang,Min Xu Conference proceedings 2023 The Editor(s) (if applicable) and The A

Dawdle

Toward Better SSIM Loss for Unsupervised Monocular Depth Estimationean absolute error (MAE) and structure similarity index measure (SSIM) with conventional form as training loss. However, they ignore the effect of different components in the SSIM function and the corresponding hyperparameters on the training. To address these issues, this work proposes a new form o

前面

SECT: Sentiment-Enriched Continual Training for Image Sentiment Analysisairs to learn high-quality visual general representations from natural language supervision. However, these models typically disregard sentiment knowledge during the pre-training phase, subsequently hindering their capacity for optimal image sentiment analysis. To address these challenges, we propos

Harbor

Learn to Enhance the Negative Information in Convolutional Neural Networkance the negative information in CNNs. In sharp contrast to ReLU which cuts off the negative neurons and suffers from the issue of “dying ReLU”, LENI enjoys the capacity to reconstruct the dead neurons and reduce the information loss. Compared to improved ReLUs, LENI introduces a learnable approach

neutrophils

Task-Agnostic Generalized Meta-learning Based on MAML for Few-Shot Bearing Fault Diagnosistoward the tasks in the meta-training phase and are less adaptable to new tasks, especially when the number of new tasks is small. To reduce the bias of the metamodel and improve its generalizability, this paper proposes a Task-Agnostic Generalized Meta-Learning (TAGML) algorithm based on Model-Agno

MOTTO

Weakly Supervised Image Matting via Patch Clustering neural networks and require a large dataset with ground-truth alpha matte to facilitate the training process. However, the alpha matte annotation process is extremely time-consuming and labor-intensive. To lift such a burden, we propose a novel deep learning-based weakly supervised image matting me

Connotation

Attention-Guided Motion Estimation for Video Compressionat attention from both industry and research, as a result of the potential of neural networks. More and more researchers are developing learned-based video compression frameworks and methods. Obviously, how to represent the motion information in the video is one of the essential questions across all

Vital-Signs

Cloud Detection from Remote Sensing Images by Cascaded U-shape Attention Networkserns, but it also brings difficulties to the information extraction from optical images, especially when the underlying surface features to be analyzed are obscured. Therefore, cloud detection is an indispensable step in optical remote sensing image processing. Different from low-spatial resolution

CAJ

inhumane

Myocarditis

GLM: A Model Based on Global-Local Joint Learning for Emotion Recognition from Gaits Using Dual-Stretudy, we propose a novel dual-stream model (GLM) for gait emotion recognition that combines the strengths of global and local features. We extract skeleton point gait data from walking videos and process them into suitable inputs for two channels of feature extraction networks, which respectively ca