Page
%P
664,859 Result(s)
-
Book Series
-
Chapter
Transformer-Driven Models for Language, Vision, and Multimodality
In this chapter, we will learn about the modeling and learning techniques that drive multimodal applications. We will focus specifically on the recent advances in transformer-based modeling for natural languag...
-
Chapter
Multimodal Content Generation
In this chapter, we will review the advances that are being made in this new field of multimodal content generation and also discuss several challenges associated with this emerging technology. First, we will ...
-
Chapter
Outlook
While multimodal information retrieval has several exciting applications and a high potential for impact on important problems, there are several challenges associated with the information that lives on the in...
-
Chapter
Introduction
In this book, our emphasis is on multimodal information retrieval, specifically concentrating on text and image data. The traditional unimodal systems, limited to a single type of data, often fall short of cap...
-
Book
-
Chapter
Multimodal Information Retrieval
In today’s rapidly evolving digital landscape, the wealth of available information has expanded beyond the boundaries of traditional text-based content. With the proliferation of multimedia platforms and data ...
-
Chapter
Retrieval Augmented Modeling
Till this point in our book, we have discussed the fundamental principles of information retrieval, exploring its key elements, and various approaches to achieving effective retrieval, including multimodal ret...
-
Article
Open AccessSignifiers for conveying and exploiting affordances: from human-computer interaction to multi-agent systems
The ecological psychologist James J. Gibson defined the notion of affordances to refer to what action possibilities environments offer to animals. In this paper, we show how (artificial) agents can discover an...
-
Article
A general framework for improving cuckoo search algorithms with resource allocation and re-initialization
Cuckoo search (CS) has currently become one of the most favorable meta-heuristic algorithms (MHAs). In this article, a simple yet effective framework is proposed for CS algorithms to reinforce their performanc...
-
Article
Tensor discriminant analysis on grassmann manifold with application to video based human action recognition
Representing videos as linear subspaces on Grassmann manifolds has made great strides in action recognition problems. Recent studies have explored the convenience of discriminant analysis by making use of Gras...
-
Article
Open AccessAn algorithmic debugging approach for belief-desire-intention agents
Debugging agent systems can be rather difficult. It is often noted as one of the most time-consuming tasks during the development of cognitive agents. Algorithmic (or declarative) debugging is a semi-automatic...
-
Article
ConDA: state-based data augmentation for context-dependent text-to-SQL
The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for...
-
Article
Fast Shrinking parents-children learning for Markov blanket-based feature selection
High-dimensional data leads to degraded performance of machine learning algorithms and weak generalization of models, so feature selection is of great importance. In a Bayesian network (BN), the Markov blanket...
-
Article
Open AccessModeling and shadowing paraconsistent BDI agents
The Bdi model of rational agency has been studied for over three decades. Many robust multiagent systems have been developed, and a number of Bdi logics have been studied. Following this intensive development pha...
-
Article
Two guidance joint network based on coarse map and edge map for camouflaged object detection
Camouflaged object detection (COD) entails identifying objects in an image that blend with the background. However, most traditional COD methods have not comprehensively considered the information provided by ...
-
Article
Combining core points and cluster-level semantic similarity for self-supervised clustering
Contrastive learning utilizes data augmentation to guide network training. This approach has attracted considerable attention for clustering, object detection, and image segmentation. However, previous studies...
-
Article
Drfnet: dual stream recurrent feature sharing network for video dehazing
The primary effects of haze on captured images/frames are visibility degradation and color disturbance. Even though extensive research has been done on the tasks of video dehazing, they fail to perform better ...
-
Article
Open AccessSliced Wasserstein adversarial training for improving adversarial robustness
Recently, deep-learning-based models have achieved impressive performance on tasks that were previously considered to be extremely challenging. However, recent works have shown that various deep learning model...
-
Article
Making model checking feasible for GOAL
Agent Programming Languages have been studied for over 20 years for programming complex decision-making for autonomous systems. The GOAL agent programming language is particularly interesting since it depends ...
