AI Medical Compendium Journal:
IEEE transactions on pattern analysis and machine intelligence

Showing 41 to 50 of 300 articles

Recognizing Object by Components With Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them can still be adaptively evaded. One reason for the weak adversarial ro...

JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments.

IEEE transactions on pattern analysis and machine intelligence
We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 RGB video at 15 fps, 3D point clouds from two 1...

Human Collective Intelligence Inspired Multi-View Representation Learning - Enabling View Communication by Simulating Human Communication Mechanism.

IEEE transactions on pattern analysis and machine intelligence
In real-world applications, we often encounter multi-view learning tasks where we need to learn from multiple sources of data or use multiple sources of data to make decisions. Multi-view representation learning, which can learn a unified representat...

EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset.

IEEE transactions on pattern analysis and machine intelligence
Multi-modal datasets in artificial intelligence (AI) often capture a third-person perspective, but our embodied human intelligence evolved with sensory input from the egocentric, first-person perspective. Towards embodied AI, we introduce the Egocent...

Explainability in Graph Neural Networks: A Taxonomic Survey.

IEEE transactions on pattern analysis and machine intelligence
Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc tec...

From Show to Tell: A Survey on Deep Learning-Based Image Captioning.

IEEE transactions on pattern analysis and machine intelligence
Connecting Vision and Language plays an essential role in Generative Intelligence. For this reason, large research efforts have been devoted to image captioning, i.e. describing images with syntactically and semantically meaningful sentences. Startin...

Deformable Protein Shape Classification Based on Deep Learning, and the Fractional Fokker-Planck and Kähler-Dirac Equations.

IEEE transactions on pattern analysis and machine intelligence
The classification of deformable protein shapes, based solely on their macromolecular surfaces, is a challenging problem in protein-protein interaction prediction and protein design. Shape classification is made difficult by the fact that proteins ar...

Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction.

IEEE transactions on pattern analysis and machine intelligence
Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on ...

Representational Gradient Boosting: Backpropagation in the Space of Functions.

IEEE transactions on pattern analysis and machine intelligence
The estimation of nested functions (i.e., functions of functions) is one of the central reasons for the success and popularity of machine learning. Today, artificial neural networks are the predominant class of algorithms in this area, known as repre...

Hyperbolic Deep Neural Networks: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the deep representations in the hyperbolic space provide high fidelity embeddings with few dimensions, especially for data possessing hierarchical structure. Such a hyper...