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

Showing 1 to 10 of 300 articles

Learning Emotion Category Representation to Detect Emotion Relations Across Languages.

IEEE transactions on pattern analysis and machine intelligence
Understanding human emotions is crucial for a myriad of applications, from psychological research to advancements in Natural Language Processing (NLP). Traditionally, emotions are categorized into distinct basic groups, which has led to the developme...

Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering.

IEEE transactions on pattern analysis and machine intelligence
Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the q...

A Causality-Aware Paradigm for Evaluating Creativity of Multimodal Large Language Models.

IEEE transactions on pattern analysis and machine intelligence
Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and d...

Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting.

IEEE transactions on pattern analysis and machine intelligence
Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical practice in diagnostic radiology. Existing methods typically rely on large-scale medical datasets an...

T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-Image Generation.

IEEE transactions on pattern analysis and machine intelligence
Despite the impressive advances in text-to-image models, they often struggle to effectively compose complex scenes with multiple objects, displaying various attributes and relationships. To address this challenge, we present T2I-CompBench++, an enhan...

HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-Body Mesh Recovery.

IEEE transactions on pattern analysis and machine intelligence
Recovering whole-body mesh by inferring the abstract pose and shape parameters from visual content can obtain 3D bodies with realistic structures. However, the inferring process is highly non-linear and suffers from image-mesh misalignment, resulting...

Prompt Tuning of Deep Neural Networks for Speaker-Adaptive Visual Speech Recognition.

IEEE transactions on pattern analysis and machine intelligence
Visual Speech Recognition (VSR) aims to infer speech into text depending on lip movements alone. As it focuses on visual information to model the speech, its performance is inherently sensitive to personal lip appearances and movements, and this make...

Human-Centric Transformer for Domain Adaptive Action Recognition.

IEEE transactions on pattern analysis and machine intelligence
We study the domain adaptation task for action recognition, namely domain adaptive action recognition, which aims to effectively transfer action recognition power from a label-sufficient source domain to a label-free target domain. Since actions are ...

Tensor Coupled Learning of Incomplete Longitudinal Features and Labels for Clinical Score Regression.

IEEE transactions on pattern analysis and machine intelligence
Longitudinal data with incomplete entries pose a significant challenge for clinical score regression over multiple time points. Although many methods primarily estimate longitudinal scores with complete baseline features (i.e., features collected at ...

Medical Federated Model With Mixture of Personalized and Shared Components.

IEEE transactions on pattern analysis and machine intelligence
Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alte...