Question-Instructed Visual Descriptions for Zero-Shot Video Answering (2024.findings-acl)
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| Challenge: | Existing models for video QA rely on complex architectures, expensive pipelines or closed models like GPTs. |
| Approach: | They propose a single instruction-aware open vision-language model to tackle videoQA using frame descriptions. |
| Outcome: | The proposed framework achieves higher performance than current state-of-the-art models on videoQA benchmarks. |
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| Challenge: | Recent studies have focused on image-based question-answering (QA) tasks, but little has been done on video-based QA. |
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Do Video Language Models really understand the video contexts? (2025.naacl-srw)
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| Challenge: | Recent advances in VideoQA performance have shown that visual language models are effective but the processes of understanding and reasoning in VLMs remain under-explored. |
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| Challenge: | a recent study has shown that short video understanding is not trivial due to the need for long-range temporal reasoning capabilities. |
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Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts (2023.findings-emnlp)
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| Challenge: | Visual question answering (VQA) is a task that requires an understanding of both the image and the question to provide a natural language answer. |
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Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training (2022.findings-emnlp)
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| Challenge: | Existing approaches require substantial adaptation of pretrained language models for vision-language reasoning tasks. |
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Video Question Answering: Datasets, Algorithms and Challenges (2022.emnlp-main)
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| 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. |
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