Source-linked AI summary
Span-based Localizing Network for Natural Language Video Localization
Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou
TL;DR
NLVL requires locating a query-matching span in an untrimmed video, but prior approaches commonly formulate it as ranking or regression. The paper treats video as a text passage for span-based QA and adds query-guided highlighting in VSLNet; experiments on three benchmark datasets report the best performance among evaluated methods.
Problem
NLVL must locate a temporal video moment matching a language query, while prior methods primarily formulate the task as ranking or regression.
Method
The paper treats video features as a passage and target moments as answer spans, then adds query-guided highlighting to a standard span-based QA framework.
Results
VSLNet achieves the best performance among evaluated methods on three benchmark datasets, with a 7.47% improvement at IoU = 0.7 versus MAN on Charades-STA.
Takeaways & Limitations
The effectiveness of VSLBase and VSLNet suggests that span-based QA is a promising direction for NLVL.
Takeaways & Limitations
Exploring pretrained models for NLVL is outside the scope of this study, despite their reported gains in span-based QA.
Abstract
from arXiv · showhide
Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods; and adopting span-based QA framework is a promising direction to solve NLVL.
1 Introduction
NLVL locates a video moment matching a language query, and this work reframes it as multimodal span-based question answering. VSLNet extends a standard QA baseline with query-guided highlighting to address video–text differences and improves performance on benchmark datasets.
- NLVL retrieves a temporal moment from an untrimmed video that semantically corresponds to a language query.
- Unlike prior ranking, regression, and reinforcement-learning formulations, the paper treats the video as a text passage and the target moment as an answer span.
- Video differs from text because adjacent frames are continuous and causally related, while small frame shifts are less meaningful than changes to individual words.
- VSLBase predicts answer-span start and end boundaries, while VSLNet adds query-guided highlighting to search within a highlighted region.
- VSLNet achieves the best performance among evaluated methods, while VSLBase delivers comparable performance to strong baselines.
2 Related Work
Prior NLVL methods use multimodal matching, regression, or sequential decision-making, while related QA-based studies do not adopt standard span-based QA. This paper positions VSLBase and VSLNet as standard-QA-based alternatives that explicitly address video–text differences, while leaving pretrained-model exploration for future work.
- Early NLVL methods rank densely sampled candidate moments, making them sensitive to negative samples and reducing efficiency and flexibility.
- Other approaches directly regress temporal boundaries or use dense bottom-up prediction, while additional work models cross-modal interactions or sequential decisions.
- Related QA-based studies consider question answering for NLVL but neither explain its differences from traditional span-based QA nor adopt the standard framework.
- The paper applies established span-based QA frameworks to NLVL by treating video features as passage representations, while noting that these frameworks were not designed for video–text differences.
- Pretrained transformer and multimodal models are identified as relevant future directions, but exploring them is outside this study's scope.
3 Methodology
The methodology recasts NLVL as multimodal span-based question answering by treating visual features as a passage and the target moment as an answer span. VSLNet extends this baseline with query-guided highlighting to focus localization on query-relevant video regions.
- 3.2 Feature Encoder: Visual features extracted by a pretrained 3D ConvNet are treated analogously to word embeddings, with n features representing passage tokens.Visual and query embeddings are projected into a shared dimension before feature encoding.
- 3.1 Span-based QA for NLVL: NLVL is transformed into SQuAD-style triples where visual features form the passage, the language query forms the question, and the target moment forms the answer span.The task becomes predicting the answer span’s start and end boundaries in the visual feature sequence.
- 3.3 Context-Query Attention: Context-query attention computes visual-query similarity and derives context-to-query and query-to-context attention weights for cross-modal interaction.The similarity matrix S is normalized row-wise and column-wise with SoftMax.
- 3.4 Conditioned Span Predictor: The conditioned span predictor uses stacked unidirectional LSTMs so end-boundary prediction can be conditioned on start-boundary information.Feed-forward layers produce start and end scores, which are converted into probability distributions with SoftMax and trained using cross-entropy.
- 3 Methodology: VSLNet is built on VSLBase by adding QGH, while VSLBase predicts start and end answer boundaries without addressing the differences between video and natural language.The complete model is trained end-to-end by minimizing the combined objective.
- 3.5 Query-Guided Highlighting: Query-Guided Highlighting treats the query-aligned target moment as foreground and the remaining video as background, extending foreground boundaries according to α.The module encodes the query into a sentence representation, concatenates it with each visual feature, predicts foreground confidence, and replaces the span predictor’s features with highlighted features.
4 Experiments
Experiments on three benchmark datasets evaluate span-based QA for NLVL, comparing VSLBase and VSLNet with existing methods and analyzing modules, highlighting, and error patterns. VSLNet achieves the strongest reported results while QGH improves localization and concentrates predictions in higher-IoU regions.
- Comparison with State-of-the-Arts: VSLNet significantly outperforms all baselines on Charades-STA, with a 7.47% improvement at IoU = 0.7 versus 0.78% at IoU = 0.5 compared to MAN.VSLBase also outperforms compared baselines at IoU = 0.7, while visual-feature fine-tuning helps VSLNet achieve state-of-the-art results.
- Comparison with State-of-the-Arts: VSLNet surpasses ExCL by 2.06% at IoU = 0.7 and 0.16% at IoU = 0.3 on ActivityNet Caption, while also surpassing other baselines.The authors caution that the comparison is not strictly direct because the experiments contain different numbers of missing videos.
- Comparison with State-of-the-Arts: VSLNet achieves new state-of-the-art performance across all evaluation metrics on TACoS, while VSLBase shows comparable performance without QGH.These results support both the span-based QA formulation and the added highlighting strategy.
- Module Analysis: CMF is consistently superior to CAT, and CQA is more effective than CAT regardless of the feature encoder; replacing the attention module yields a 3.09% gain versus 1.61% for the encoder.The module study identifies CQA as the more important component under R@1, IoU = 0.7.
- QGH and Qualitative Analysis: QGH improves performance across extension ratios from α = 0 to ∞, with optimal performance between α = 0.05 and 0.2; it also produces more high-IoU predictions than VSLBase.Qualitative examples show VSLNet localizes moments closer to ground truth and constrains boundaries within highlighted regions, while failures include confusing related actions and incomplete multi-action queries.
5 Conclusion
The paper shows that treating video as a text passage enables a multimodal span-based QA formulation for NLVL. VSLNet adds query-guided highlighting to search within a predicted coarse region, and results support this direction.
- VSLBase effectively addresses NLVL by treating the target moment as an answer span in a standard span-based QA framework.
- VSLNet extends VSLBase with query-guided highlighting, guiding answer search within a predicted coarse region.
- The approach addresses two major differences between video and text through the added highlighting strategy.
- Experiments show that VSLNet and even VSLBase support exploring span-based QA frameworks for NLVL.