Challenge: sparse sampling of videos suffers from inter-modal redundancy and visual redundancies . et al., 2021) proposes to sparsestly sample frames from videos to alleviate temporal redundance .
Approach: They propose to use sparse sampling to alleviate temporal redundancy in videos . they propose to penalize high-redundant video patches and text tokens .
Outcome: The proposed method improves on four benchmark datasets.

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Challenge: Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries.
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Challenge: despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck.
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Challenge: Recent large-scale video-language pre-trained models have shown appealing performance on downstream tasks.
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Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube (2020.emnlp-main)

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Challenge: Recent work adopts a "pre-training + fine-tuning" approach for zero-shot transfer to end tasks without fine- tuning.
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Challenge: Multimodal Large Language Models (MLLMs) are limited by context length when processing long videos.
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PruneVid: Visual Token Pruning for Efficient Video Large Language Models (2025.findings-acl)

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Challenge: Existing approaches to video token pruning face significant computational challenges due to the redundancy inherent in video data.
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VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding (2021.findings-acl)

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Challenge: Existing methods for multimodal video understanding are task-specific, limiting their use for retrieval-style end tasks.
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TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding (2023.findings-emnlp)

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Challenge: Experimental results show that TESTA reduces the number of visual tokens by 75% and thus accelerates video encoding.
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