| Challenge: | a novel model for localizing moments in a longer video using natural language queries is challenging . moment localization is similar to other language and vision tasks, but it offers an interesting opportunity to model temporal dependencies and reasoning in text. |
| Approach: | They propose a model that explicitly reasons about different temporal segments in a video . their dataset includes a dataset with real videos and template sentences . |
| Outcome: | The proposed model explicitly reasons about different temporal segments in a video . it shows that temporal context is important for localizing phrases which include temporal language . |
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Memory-efficient Temporal Moment Localization in Long Videos (2023.eacl-main)
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| Challenge: | Temporal Moment Localization is a multi-modal task that requires understanding the temporal relationships in the entire input video. |
| Approach: | They propose a stochastic sampling module that can process long videos at a constant memory footprint. |
| Outcome: | The proposed model can process videos as long as 18 minutes at a constant memory footprint and achieves faster and faster results than competing models. |
TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos (2026.findings-acl)
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| Challenge: | Several video understanding applications require the ability of temporal reasoning. |
| Approach: | They propose a video large language model for temporal reasoning and fine-grained understanding in long videos. |
| Outcome: | The proposed model outperforms existing methods in time and motion studies and temporal action segmentation evaluations. |
MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction (2023.acl-long)
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| Challenge: | Natural language video localization (NLVL) aims to localize a temporal moment from an untrimmed video that semantically corresponds to a given text query. |
| Approach: | They propose a proposal-based solution that generates proposals and selects the best matching proposal. |
| Outcome: | The proposed solution is faster than existing approaches on three public datasets. |
Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network (2020.coling-main)
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| Challenge: | Existing methods for temporal sentence localization in videos focus on visual content, but they are insufficient to model complex video contents. |
| Approach: | They propose a deep rectification-modulation network to correct attention misalignment . they use sentence information to capture frame-to-frame relation . |
| Outcome: | The proposed method achieves state-of-the-art performance on three public datasets. |
Mitigating the Discrepancy Between Video and Text Temporal Sequences: A Time-Perception Enhanced Video Grounding method for LLM (2025.coling-main)
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| Challenge: | Existing video LLMs excel at capturing the overall description of a video but lack the ability to demonstrate an understanding of temporal dynamics and localized content within the video. |
| Approach: | They propose a Time-Perception Enhanced Video Grounding via Boundary Perception and Temporal Reasoning to improve LLMs' understanding of video temporality. |
| Outcome: | The proposed method improves on three datasets: ActivityNet, Charades, and DiDeMo (up to 11.2% improvement on R@0.3). |
Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Temporal reasoning is a vital component of human communication and understanding, yet remains an underexplored area within the context of Large Language Models (LLMs). |
| Approach: | They propose to use 3 prompting strategies to evaluate 8 different LLMs across 6 datasets and 2 Code Generation LMs to perform the analysis. |
| Outcome: | The proposed models perform better on NLP tasks than the standard models on the same dataset. |
TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)
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| Challenge: | Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats . |
| Approach: | They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect . |
| Outcome: | The proposed benchmarks show that video large language models exhibit poor temporal perception ability. |
Natural Language Video Localization with Learnable Moment Proposals (2021.emnlp-main)
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| Challenge: | Existing methods for video moment localization have poor performance due to predefined rules. |
| Approach: | They propose a model with a fixed set of learnable moment proposals with 'border-aware loss' they propose to localize the video moment corresponding to the query by locating the start and end timestamps in an untrimmed video. |
| Outcome: | The proposed model outperforms state-of-the-art models on two challenging benchmarks. |
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)
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Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang
| Challenge: | Video Large Language Models (VLMs) have been praised for their performance in coarse-grained video understanding but still face ineffective temporal grounding and inadequate timestamp representations. |
| Approach: | They propose a novel Video-LLM that senses and reasoned over specific video moments with fine-grained temporal precision. |
| Outcome: | The proposed model surpasses existing models in fine-grained video understanding tasks and exhibits strong potential as a general video understanding assistant. |
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information (2025.acl-long)
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| Challenge: | Temporal Heads are attention heads that primarily handle temporal knowledge. |
| Approach: | They discover Temporal Heads, specific attention heads that primarily handle temporal knowledge, through circuit analysis. |
| Outcome: | The proposed models can handle temporal knowledge without compromising time-invariant and question-answering performances. |