| Challenge: | Recent advances in video question answering have led to a surge in popularity . despite the popularity, VideoQA remains one of the greatest challenges . |
| Approach: | They categorize the video question-answer datasets into normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA according to the modalities invoked in the question-announcement pairs. |
| Outcome: | The proposed methods are mainly designed for Factoid QA and inference VideoQA . the proposed methods have been compared with other methods and are robust and interpretable. |
Similar Papers
TVQA: Localized, Compositional Video Question Answering (D18-1)
Copied to clipboard
| Challenge: | Recent studies have focused on image-based question-answering (QA) tasks, but little has been done on video-based QA. |
| Approach: | They present a large-scale video QA dataset based on 6 popular TV shows . they provide analysis of the new dataset and trainable neural network framework . |
| Outcome: | The proposed dataset includes 152,545 QA pairs from 21,793 clips spanning over 460 hours of video. |
LifeQA: A Real-life Dataset for Video Question Answering (2020.lrec-1)
Copied to clipboard
Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
| Challenge: | Existing video question answering datasets consist of movies and TV shows, but they are not representative of our day-to-day lives. |
| Approach: | They propose a benchmark dataset for video question answering that focuses on day-to-day situations. |
| Outcome: | The proposed dataset analyzes the challenging but realistic aspects of LifeQA . it consists of video clips and over 2.3k multiple-choice questions . |
VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for video question answering align visual or textual features directly with large language models, limiting the deep semantic association between modalities and hindering a comprehensive understanding of interactions within spatial and temporal contexts. |
| Approach: | They propose a temporal-aware framework for multi-modal video question answering that aligns videos and questions at fine-grained levels. |
| Outcome: | The proposed framework improves reasoning ability and accuracy of videoQA by aligning videos and questions at fine-grained levels. |
TutorialVQA: Question Answering Dataset for Tutorial Videos (2020.lrec-1)
Copied to clipboard
| Challenge: | a new question answering task on instructional videos is needed due to their verbose nature . factoid questions are only a small part of what people actually want to ask on video contents . |
| Approach: | They propose a question answering task on instructional videos based on video transcripts . they use a dataset consisting of 6,000 manually collected triples of (video, question, answer span) |
| Outcome: | The proposed task focuses on screencast tutorial videos pertaining to an image editing program. |
TVQA+: Spatio-Temporal Grounding for Video Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Existing video QA datasets only contain QA pairs without labels for key clips or regions needed to answer the question. |
| Approach: | They propose a framework that grounds evidence in both spatial and temporal domains to answer questions about videos using bounding boxes. |
| Outcome: | The proposed framework can produce interpretable spatio-temporal attention visualizations. |
Rethinking Multi-Modal Alignment in Multi-Choice VideoQA from Feature and Sample Perspectives (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to VideoQA focus on utilizing frame- or object-level visual representations, but they neglect visual-language interactions. |
| Approach: | They propose to break down video into trajectories and first leverage trajectory feature in VideoQA to enhance alignment between two modalities. |
| Outcome: | The proposed method outperforms all the state-of-the-art models on the NExT-QA benchmark. |
In-the-Wild Video Question Answering (2022.coling-1)
Copied to clipboard
| Challenge: | Existing video understanding datasets focus on human interactions with little attention being paid to the “in the wild” settings. |
| Approach: | They propose a video understanding dataset of videos recorded outdoors . they propose identifying visual support for a given question and answer . |
| Outcome: | The proposed dataset examines the ability of models to understand videos, including video question answering, video captioning, and fill-inthe-blank tasks. |
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to VideoQA often fail when complex reasoning or temporal relationships are involved. |
| Approach: | They propose a method that leverages reasoning processes generated by Multimodal Large Language Models to improve VideoQA models. |
| Outcome: | The proposed method improves VideoQA models on three benchmarks. |
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to visual question answering (VQA) are not suitable for real-world applications. |
| Approach: | They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets. |
Mulan: A Multi-Level Alignment Model for Video Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focus on visual-language alignment at the video level, but they do not account for fine-grained semantic interaction between video and text. |
| Approach: | They propose a multi-level Alignment Model for Video Question Answering that establishes alignment between visual and textual modalities at the object-level, frame-level and video-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods even with a small amount of extra visual-language pre-training data and a reduced number of trainable parameters. |