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Found 8 papers, 0 papers with code
Zero-Shot Aerial Object Detection with Visual Description Regularization
Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning.
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
The advent of Industry 4. 0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known as industrial intelligence.
VCL Challenges 2023 at ICCV 2023 Technical Report: Bi-level Adaptation Method for Test-time Adaptive Object Detection
This report outlines our team's participation in VCL Challenges B Continual Test_time Adaptation, focusing on the technical details of our approach.
Attribute Localization and Revision Network for Zero-Shot Learning
Second, we propose Attribute Revision Module (ARM), which generates image-level semantics by revising the ground-truth value of each attribute, compensating for performance degradation caused by ignoring intra-class variation.
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model
Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically.
Partial Differential Equations Meet Deep Neural Networks: A Survey
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling.
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased towards the seen class.
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning. While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance. Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model.
