Check out this awesome Streamlit app I built
https://quickreads.streamlit.app/
1 Like
Welcome to our QuickReads app! This app is designed to help you quickly and easily read long or complex texts. With our app, you can create detailed summaries , extract key points , and extract entities from any text you provide. Whether you’re a student trying to condense a lengthy reading assignment, a professional looking to quickly read a document, or just someone who wants to understand a text better, our app has you covered. Simply paste your text into the app, and our advanced algorithms will do the rest, providing you with a clear and concise summary of the text in just a few seconds.
Potential Use Cases
- Students: Generating summaries of long texts to help quickly understand and retain important information,and preparing for exams by quickly reviewing key points from texts.
- Educators: Creating summaries and key points from texts to help their students learn more effectively, and using entity extraction to understand the context and relationships between different ideas in a text.
- Professionals: Summarizing long documents to quickly understand the key points and main ideas, and using entity extraction to identify important people, places, and concepts in a text.
- Researchers: Summarizing research papers and other technical documents to quickly identify the key points and main contributions, and using entity extraction to understand the relationships between different ideas in a text.
- General readers: Summarizing long articles or books to quickly understand the main ideas and key points, and using entity extraction to help improve reading comprehension and retention.
Overall, the app can be a useful tool for a wide range of users to help them quickly understand and retain important information from complex texts.
Example
Input text:
Visual explanation enables humans to understand the decision making of deep convolutional neural network (CNN), but it is insufficient to contribute to improving CNN performance. In this paper, we focus on the attention map for visual explanation, which represents a high response value as the attention location in image recognition. This attention region significantly improves the performance of CNN by introducing an attention mechanism that focuses on a specific region in an image. In this work, we propose Attention Branch Network (ABN), which extends a response-based visual explanation model by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for the attention mechanism and is trainable for visual explanation and image recognition in an end-to-end manner. We evaluate ABN on several image recognition tasks such as image classification, fine-grained recognition, and multiple facial attribute recognition. Experimental results indicate that ABN outperforms the baseline models on these image recognition tasks while generating an attention map for visual explanation. Our code is available.
Simplified Summary:
This text describes a new method for understanding how artificial intelligence makes decisions when recognizing images. The method, called Attention Branch Network, helps improve the accuracy of image recognition and can explain why certain decisions were made.
Detailed Summary:
The text discusses the use of deep convolutional neural networks (CNNs) for image recognition and the challenges associated with understanding the decision-making process of these networks. The authors propose a new approach called Attention Branch Network (ABN), which extends a response-based visual explanation model by introducing a branch structure with an attention mechanism. ABN outperforms existing models on several image recognition tasks, and can generate an attention map for visual explanation.
Key Points:
- Deep convolutional neural networks (CNNs) are used for image recognition
- Understanding the decision-making process of CNNs is challenging
- Attention Branch Network (ABN) is a new approach that improves the performance of CNNs
- ABN generates an attention map for visual explanation
- ABN outperforms existing models on several image recognition tasks
Extracted Notes (entities):
- visual_explanation:
- enables_humans: ["understand decision-making of deep convolutional neural networks (CNN)"]
- insufficient: ["contribute to improving CNN performance"]
- attention_map:
- visual_explanation: ["represents high response value as attention location in image recognition"]
- improves_performance: ["introducing attention mechanism that focuses on specific region in image"]
- attention_branch_network (ABN):
- extends_response-based_visual_explanation_model: ["introducing branch structure with attention mechanism"]
- applicable_to_image_recognition_tasks: ["introducing branch for attention mechanism"]
- trainable: ["visual explanation and image recognition in end-to-end manner"]
- evaluation:
- tasks: ["image classification", "fine-grained recognition", "multiple facial attribute recognition"]
- outperforms_baseline_models: ["generating attention map for visual explanation"]
- code: ["available"]
1 Like
Check it out! No sign ups. Read your article or text summarised instantly. Feedbacks and new feature ideas will be appreciated.
Here is the new Interface
https://quickreads.streamlit.app/
Link to the reader:
https://quickreads.streamlit.app/Onboarding
This topic was automatically closed 365 days after the last reply. New replies are no longer allowed.