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. |
Similar Papers
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. |
Scene-robust Natural Language Video Localization via Learning Domain-invariant Representations (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on improving performance with the assumption of independently identical data distribution while ignoring out-of-distribution data. |
| Approach: | They propose a scene-robust NLVL problem and a generalizable framework to learn a robust model. |
| Outcome: | The proposed model learns generalizable domain-invariant representations by alignment and decomposition. |
Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos (2021.emnlp-main)
Copied to clipboard
| Challenge: | Temporal sentence localization in videos is an important yet challenging task in natural language processing. |
| Approach: | They propose an Adaptive Proposal Generation Network to maintain the segment-level interaction while speeding up the efficiency. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three challenging benchmarks. |
WSLLN:Weakly Supervised Natural Language Localization Networks (D19-1)
Copied to clipboard
| Challenge: | Existing methods to learn correspondence between visual segments and texts require temporal coordinates for training, which leads to high costs of annotation. |
| Approach: | They propose weakly supervised language localization networks to detect events in untrimmed videos . they train with only video-sentence pairs without accessing to temporal locations of events . |
| Outcome: | Experiments on ActivityNet Captions and DiDeMo show that WSLLN performs state-of-the-art. |
Span-based Localizing Network for Natural Language Video Localization (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to NLVL are either ranking tasks or regressing the target video span. |
| Approach: | They propose a video span localizing network to solve a natural language video localization task using a span-based QA approach. |
| Outcome: | The proposed network outperforms the state-of-the-art methods on three benchmark datasets. |
Exploiting Intrinsic Multilateral Logical Rules for Weakly Supervised Natural Language Video Localization (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for WS-NLVL rarely consider complex temporal relations enclosing the language query, yielding illogical predictions. |
| Approach: | They propose a plug-and-play method to exploit temporal relations and logical rules for WS-NLVL. |
| Outcome: | The proposed method is able to retrieve the moment corresponding to a language query in a video with only video-language pairs utilized during training. |
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 . |
ExCL: Extractive Clip Localization Using Natural Language Descriptions (N19-1)
Copied to clipboard
| Challenge: | Prior approaches to retrieving clips within videos based on a given query are inefficient and text-clip similarity driven ranking-based approaches are far more complicated. |
| Approach: | They propose an extractive approach that extracts the start and end frames by leveraging cross-modal interactions between the text and video to generate a joint representation. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art on two datasets and has comparable performance on a third. |
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing annotated training datasets hinder development of supervised learning models for multimedia content . lack of annotating benchmarks hinders development of models with satisfactory performance . a recent study shows that large language models have zero-shot performance in multimedia understanding . |
| Approach: | They propose to verbalize long videos to generate their descriptions in natural language . they then perform video-understanding tasks on the generated story as opposed to the original video . |
| Outcome: | The proposed method achieves better results than baselines for video understanding. |
Generating Structured Pseudo Labels for Noise-resistant Zero-shot Video Sentence Localization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing zero-shot pipelines generate event proposals and then generate a pseudo query for each event proposal. |
| Approach: | They propose a Structure-based Pseudo Label generation (SPL) that generates free-form interpretable pseudo queries before constructing query-dependent event proposals. |
| Outcome: | The proposed method learns with only video data without any annotation . it generates free-form interpretable pseudo queries before constructing query-dependent event proposals . |