| 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. |
Similar Papers
Localizing Moments in Video with Temporal Language (D18-1)
Copied to clipboard
| 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 . |
TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos (2026.findings-acl)
Copied to clipboard
| 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. |
Mitigating the Discrepancy Between Video and Text Temporal Sequences: A Time-Perception Enhanced Video Grounding method for LLM (2025.coling-main)
Copied to clipboard
| 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). |
MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction (2023.acl-long)
Copied to clipboard
| 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. |
See More, Store Less: Memory-Efficient Resolution for Video Moment Retrieval (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing video moment retrieval methods rely on sparse frame sampling, risking information loss. |
| Approach: | a new video-based framework enhances memory efficiency while maintaining high information resolution . SMORE uses query-guided captions to encode semantics aligned with user intent . |
| Outcome: | a new framework improves memory efficiency while maintaining high information resolution . it achieves state-of-the-art performance on QVHighlights, Charades-STA, and ActivityNet-Captions benchmarks . |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
Copied to clipboard
Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Long video understanding presents unique challenges due to the complexity of reasoning over extended timespans. |
| Approach: | They propose a framework VideoINSTA to leverage large language models for video understanding . they propose 'event-based temporalreasoning' and 'content-based spatial reasoning' |
| Outcome: | The proposed model significantly improves state-of-the-art on three long video question-answering benchmarks. |
Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have shown promising results in video corpus moment retrieval . however, they relied on the expensive query annotations for the VCMR . |
| Approach: | They propose a self-supervised learning framework to localize video corpus moment without annotations. |
| Outcome: | The proposed framework can localize the video corpus moment without any explicit annotation on TVR dataset. |
Natural Language Video Localization with Learnable Moment Proposals (2021.emnlp-main)
Copied to clipboard
| 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. |
DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Temporal Language Grounding (TLG) is a task to determine temporal boundaries of video moments that correspond to a language query. |
| Approach: | They propose an energy-based model framework to explicitly learn moment-query distributions. |
| Outcome: | The proposed model outperforms the state-of-the-art models on four public temporal language grounding datasets. |