AIMC Topic: Neural Networks, Computer

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Towards zero-shot human-object interaction detection via vision-language integration.

Neural networks : the official journal of the International Neural Network Society
Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. Such heavy...

Graph Neural Networks with Coarse- and Fine-Grained Division for mitigating label noise and sparsity.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mechanism, which largely relies on the availability of clean labels. Howe...

Ternary spike-based neuromorphic signal processing system.

Neural networks : the official journal of the International Neural Network Society
Deep Neural Networks (DNNs) have been successfully implemented across various signal processing fields, resulting in significant enhancements in performance. However, DNNs generally require substantial computational resources, leading to significant ...

PanoGen++: Domain-adapted text-guided panoramic environment generation for vision-and-language navigation.

Neural networks : the official journal of the International Neural Network Society
Vision-and-language navigation (VLN) tasks require agents to navigate three-dimensional environments guided by natural language instructions, offering substantial potential for diverse applications. However, the scarcity of training data impedes prog...

Hy-DeFake: Hypergraph neural networks for detecting fake news in online social networks.

Neural networks : the official journal of the International Neural Network Society
Nowadays social media is the primary platform for people to obtain news and share information. Combating online fake news has become an urgent task to reduce the damage it causes to society. Existing methods typically improve their fake news detectio...

Augmenting sparse behavior data for user identity linkage with self-generated by model and mixup-generated samples.

Neural networks : the official journal of the International Neural Network Society
The user identity linkage task aims to associate user accounts belonging to the same individual by utilizing user data. This task is relevant in domains such as recommendation systems, where user-generated content (i.e., behavioral data) serves as th...

FedELR: When federated learning meets learning with noisy labels.

Neural networks : the official journal of the International Neural Network Society
Existing research on federated learning (FL) usually assumes that training labels are of high quality for each client, which is impractical in many real-world scenarios (e.g., noisy labels by crowd-sourced annotations), leading to dramatic performanc...

FingerPoseNet: A finger-level multitask learning network with residual feature sharing for 3D hand pose estimation.

Neural networks : the official journal of the International Neural Network Society
Hand pose estimation approaches commonly rely on shared hand feature maps to regress the 3D locations of all hand joints. Subsequently, they struggle to enhance finger-level features which are invaluable in capturing joint-to-finger associations and ...

Heterogeneous Graph Neural Network with Adaptive Relation Reconstruction.

Neural networks : the official journal of the International Neural Network Society
Topological structures of real-world graphs often exhibit heterogeneity involving diverse nodes and relation types. In recent years, heterogeneous graph learning methods utilizing meta-paths to capture composite relations and guide neighbor selection...

ADAMT: Adaptive distributed multi-task learning for efficient image recognition in Mobile Ad-hoc Networks.

Neural networks : the official journal of the International Neural Network Society
Distributed machine learning in mobile adhoc networks faces significant challenges due to the limited computational resources of devices, non-IID data distribution, and dynamic network topology. Existing approaches often rely on centralized coordinat...