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. |
| Approach: | They propose a method to condense video semantics by aggregating similar frames and patches within each frame. |
| Outcome: | The proposed method reduces visual tokens by 75% and accelerates video encoding. |
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| 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. |
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| Challenge: | Existing video-language models rely on concatenating visual tokens with textual inputs for joint modeling, but this method suffers from significant inefficiency when scaling to long videos with dense visual inputs. |
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| Challenge: | Recent large-scale video-language pre-trained models have shown appealing performance on downstream tasks. |
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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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| Challenge: | Current models struggle with long-form videos due to the quadratic complexity of attention mechanisms. |
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| Challenge: | Several video understanding applications require the ability of temporal reasoning. |
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METok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding (2025.emnlp-main)
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| Challenge: | Recent advances in Video Large Language Models (VLLMs) have significantly enhanced their ability to understand video content. |
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| Challenge: | Long video understanding presents unique challenges due to the complexity of reasoning over extended timespans. |
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Vista-LLM: Decoupled Query-Guided Visual Token Pruning for Efficient Long-Video Large Language Models (2026.acl-long)
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| Challenge: | Long-video understanding is bottlenecked by the high cost of processing massive visual tokens. |
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Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding (2023.findings-acl)
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Erica Kido Shimomoto, Edison Marrese-Taylor, Hiroya Takamura, Ichiro Kobayashi, Hideki Nakayama, Yusuke Miyao
| Challenge: | Recent studies have improved query inputs with pre-trained language models, but the effects of this integration are unclear. |
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