Nature methods
Nov 7, 2022
Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally fo...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
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...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
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...
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
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 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
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...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate catego...