Debiasing visual question and answering with answer preference
Visual Question Answering (VQA) requires models to generate a reasonable answer with given an image and corresponding question. It requires strong reasoning capabilities for two kinds of input features, namely image and question. However, most state-of-the-art results heavily rely on superficial cor...
Main Author: | Zhang, Xinye |
---|---|
Other Authors: | Zhang Hanwang |
Format: | Final Year Project (FYP) |
Language: | English |
Published: |
Nanyang Technological University
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/137906 |
Similar Items
-
Causalqa: a causal framework for question answering
by: Dutta, Angshuk
Published: (2022) -
Towards a hierarchical framework for predicting the best answer in a question answering system
by: Chua, Alton Yeow Kuan, et al.
Published: (2009) -
Clinical Question-Answering over Distributed EHR Data
by: Jiang, Emily
Published: (2024) -
PromptChart: prompting InstructGPT for zero & few-shot chart question answering and summarization
by: Do, Xuan Long
Published: (2023) -
CAT: enhancing multimodal large language model to answer questions in dynamic audio-visual scenarios
by: Ye, Q, et al.
Published: (2024)