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Found 17 papers, 7 papers with code
Query-based Video Summarization with Pseudo Label Supervision
Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models.
Causal Video Summarizer for Video Exploration
Multi-modal video summarization has a video input and a text-based query input.
Causalainer: Causal Explainer for Automatic Video Summarization
In this work, a Causal Explainer, dubbed Causalainer, is proposed to address this issue.
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions
This work proposes a new method that utilizes semantically related questions, referred to as basic questions, acting as noise to evaluate the robustness of VQA models.
The Dawn of Quantum Natural Language Processing
In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing.
Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"
Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency.
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
Traditional video summarization methods generate fixed video representations regardless of user interest.
Contextualized Keyword Representations for Multi-modal Retinal Image Captioning
A traditional medical image captioning model creates a medical description only based on a single medical image input.
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset.
Query-controllable Video Summarization
In this work, we introduce a method which takes a text-based query as input and generates a video summary corresponding to it.
Assessing the Robustness of Visual Question Answering Models
In this work, we propose a new method that uses semantically related questions, dubbed basic questions, acting as noise to evaluate the robustness of VQA models.
Synthesizing New Retinal Symptom Images by Multiple Generative Models
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision.
Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.
A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.
A Novel Framework for Robustness Analysis of Visual QA Models
In VQA, adversarial attacks can target the image and/or the proposed main question and yet there is a lack of proper analysis of the later.
Robustness Analysis of Visual QA Models by Basic Questions
Visual Question Answering (VQA) models should have both high robustness and accuracy.
VQABQ: Visual Question Answering by Basic Questions
Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question.
