| import streamlit as st | |
| import streamlit.components.v1 as components | |
| def run_home() -> None: | |
| """ | |
| Displays the home page for the Knowledge-Based Visual Question Answering (KB-VQA) project using Streamlit. | |
| This function sets up the main home page for demonstrating the project. | |
| Returns: | |
| None | |
| """ | |
| st.markdown(""" | |
| <div style="text-align: justify;"> | |
| \n\n\n**Welcome to the interactive application for the Knowledge-Based Visual Question Answering (KB-VQA) | |
| project. This application is an integral part of a | |
| [Master’s dissertation in Artificial Intelligence](https://info.online.bath.ac.uk/msai/) at the | |
| [University of Bath](https://www.bath.ac.uk/). As we delve into the fascinating world of VQA, I invite you | |
| to explore the intersection of visual perception, language understanding, and cutting-edge AI research.** | |
| </div>""", | |
| unsafe_allow_html=True) | |
| st.markdown("### Background") | |
| with st.expander("Read Background"): | |
| st.write(""" | |
| <div style="text-align: justify;"> | |
| Since its inception by **Alan Turing** in 1950, the **Turing Test** has been a fundamental benchmark for | |
| evaluating machine intelligence against human standards. As technology evolves, so too must the criteria | |
| for assessing AI. The **Visual Turing Test** represents a modern extension that includes visual cognition | |
| within the scope of AI evaluation. At the forefront of this advancement is **Visual Question Answering | |
| (VQA)**, a field that challenges AI systems to perceive, comprehend, and articulate insights about | |
| visual inputs in natural language. This progression reflects the complex interplay between perception | |
| and cognition that characterizes human intelligence, positioning VQA as a crucial metric for gauging | |
| AI’s ability to emulate human-like understanding. | |
| Mature VQA systems hold transformative potential across various domains. In robotics, VQA systems can | |
| enhance autonomous decision-making by enabling robots to interpret and respond to visual cues. In | |
| medical imaging and diagnosis, VQA systems can assist healthcare professionals by accurately | |
| interpreting complex medical images and providing insightful answers to diagnostic questions, thereby | |
| enhancing both the speed and accuracy of medical assessments. In manufacturing, VQA systems can optimize | |
| quality control processes by enabling automated systems to identify defects and ensure product | |
| consistency with minimal human intervention. These advancements underscore the importance of developing | |
| robust VQA capabilities, as they push the boundaries of the Visual Turing Test and bring us closer to | |
| achieving true human-like AI cognition. | |
| Unlike other vision-language tasks, VQA requires many CV sub-tasks to be solved in the process, | |
| including: **Object recognition**, **Object detection**, **Attribute classification**, **Scene | |
| classification**, **Counting**, **Activity recognition**, **Spatial relationships among objects**, | |
| and **Commonsense reasoning**. These VQA tasks often do not require external factual knowledge and only | |
| in rare cases require common-sense reasoning. Furthermore, VQA models cannot derive additional knowledge | |
| from existing VQA datasets should a question require it, therefore **Knowledge-Based Visual Question | |
| Answering (KB-VQA)** has been introduced. KB-VQA is a relatively new extension to VQA with datasets | |
| representing a knowledge-based VQA task where the visual question cannot be answered without external | |
| knowledge, where the essence of this task is centred around knowledge acquisition and integration with | |
| the visual contents of the image. | |
| </div>""", | |
| unsafe_allow_html=True) | |
| st.write(""" | |
| <div style="text-align: justify;"> | |
| This application showcases the advanced capabilities of the KB-VQA model, empowering users to seamlessly | |
| upload images, pose questions, and obtain answers derived from both visual and textual data. | |
| By leveraging sophisticated Multimodal Learning techniques, this project bridges the gap between visual | |
| perception and linguistic interpretation, effectively merging these modalities to provide coherent and | |
| contextually relevant responses. This research not only showcases the cutting-edge progress in artificial | |
| intelligence but also pushes the boundaries of AI systems towards passing the **Visual Turing Test**, where | |
| machines exhibit **human-like** understanding and reasoning in processing and responding to visual | |
| information. | |
| ### Tools: | |
| - **Dataset Analysis**: Provides an overview of the KB-VQA datasets and displays various analysis of the | |
| OK-VQA dataset. | |
| - **Model Architecture**: Displays the model architecture and accompanying abstract and design details for | |
| the Knowledge-Based Visual Question Answering (KB-VQA) model. | |
| - **Results**: Manages the interactive Streamlit demo for visualizing model evaluation results and analysis. | |
| It provides an interface for users to explore different aspects of the model performance and evaluation | |
| samples. | |
| - **Run Inference**: This tool allows users to run inference to test and use the fine-tuned KB-VQA model | |
| using various configurations. | |
| </div>""", | |
| unsafe_allow_html=True) | |
| st.markdown("<br>" * 1, unsafe_allow_html=True) | |
| st.write(" ##### Developed by: [Mohammed Bin Ali AlHaj](https://www.linkedin.com/in/m7mdal7aj)") | |
| st.markdown("<br>" * 2, unsafe_allow_html=True) | |
| st.write(""" | |
| **Credit:** | |
| * The project uses [LLaMA-2](https://ai.meta.com/llama/) for its reasoning capabilities and implicit knowledge | |
| to derive answers from the supplied visual context. It is made available under | |
| [Meta LlaMA license](https://ai.meta.com/llama/license/). | |
| * This application is built on [Streamlit](https://streamlit.io), providing an interactive and user-friendly | |
| interface. | |
| """) | |