PROJECT TITLE :

Attention in Reasoning Dataset, Analysis, and Modeling

ABSTRACT:

Although attention has become an increasingly popular component in deep neural networks for the purpose of both interpreting data and improving the performance of models, relatively little research has been done to investigate how attention develops throughout the completion of a task and whether it is reasonable for attention to develop in this way. In this body of work, we propose an Attention with Reasoning capability (AiR) framework, which makes use of attention in order to comprehend and enhance the process that leads to task outcomes. We begin by defining an evaluation metric that is based on a series of atomic reasoning operations. This makes it possible to conduct a quantitative measurement of attention that takes into account the reasoning process. The next step is for us to collect data on human eye-tracking and answer accuracy, after which we conduct an analysis of a variety of machine and human attention mechanisms, focusing on their capacity for reasoning and how they influence task performance. We propose supervising the learning of attention progressively along the reasoning process and differentiating between correct and incorrect attention patterns in order to improve the attention and reasoning ability of visual question answering models. This will allow us to improve the attention of the models and their ability to reason. We show that the proposed framework is effective in analyzing and modeling attention, leading to improvements in both reasoning ability and task performance.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : RDMN: A Relative Density Measure Based on MST Neighborhood for Clustering Multi-Scale Datasets ABSTRACT: Techniques for discovering intrinsic clusters that are based on density do so by classifying the regions
PROJECT TITLE : Multi-tier Workload Consolidations in the Cloud Profiling, Modeling and Optimization ABSTRACT: It is becoming increasingly important to cut down on tail latency in order to improve the experience that users have
PROJECT TITLE : RAVIR A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis ABSTRACT: The vasculature of the retina offers crucial hints that can be used in the diagnosis and ongoing monitoring of
PROJECT TITLE : Tufts Dental Database A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems
PROJECT TITLE : The Importance of Context When Recommending TV Content Dataset and Algorithms ABSTRACT: Home entertainment systems are used in a variety of settings with one or more concurrent users, with the complexity of selecting

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry