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Found 8 papers, 3 papers with code
YTCommentQA: Video Question Answerability in Instructional Videos
Experiments with answerability classification tasks demonstrate the complexity of YTCommentQA and emphasize the need to comprehend the combined role of visual and script information in video reasoning.
Multimodal Subtask Graph Generation from Instructional Videos
Real-world tasks consist of multiple inter-dependent subtasks (e. g., a dirty pan needs to be washed before it can be used for cooking).
Unsupervised Task Graph Generation from Instructional Video Transcripts
This work explores the problem of generating task graphs of real-world activities.
RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects
We test the effectiveness of our representation on the human image harmonization task by predicting shading that is coherent with a given background image.
Adversarial Defense via Learning to Generate Diverse Attacks
With the remarkable success of deep learning, Deep Neural Networks (DNNs) have been applied as dominant tools to various machine learning domains.
Diversity-Sensitive Conditional Generative Adversarial Networks
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).
Video Prediction with Appearance and Motion Conditions
Video prediction aims to generate realistic future frames by learning dynamic visual patterns.
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
In this paper, we focus on extending VQA to the video domain and contribute to the literature in three important ways.
Ranked #30 on
Visual Question Answering (VQA)
on MSRVTT-QA
