IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Text based Visual Question Answering (TextVQA) is a recently raised challenge requiring models to read text in images and answer natural language questions by jointly reasoning over the question, textual information and visual content. Introduction o...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Using cross validation to select the best model from a library is standard practice in machine learning. Similarly, meta learning is a widely used technique where models previously developed are combined (mainly linearly) with the expectation of impr...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e., pre-training a model for image classificatio...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
We propose a novel unified frameork for automated distributed active learning (AutoDAL) to address multiple challenging problems in active learning such as limited labeled data, imbalanced datasets, automatic hyperparameter selection as well as scala...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and natural illumination from a single image of an object of known geometry. The BRDF is expressed with ...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology t...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separ...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and m...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumpt...