Measure Twice, Cut Once: Grasping Video Structures and Event Semantics with LLMs for Video Temporal Localization

• URL: http://arxiv.org/abs/2503.09027v1
• Date: Wed, 12 Mar 2025 03:33:50 GMT
• Title: Measure Twice, Cut Once: Grasping Video Structures and Event Semantics with LLMs for Video Temporal Localization
• Authors: Zongshang Pang, Mayu Otani, Yuta Nakashima,
• Abstract summary: We introduce MeCo, a timestamp-free framework for temporal localization tasks.<n>MeCo partitions videos into holistic event and transition segments based on the proposed structural token generation and grounding pipeline.<n>We propose a query-focused captioning task that compels the LLM to extract fine-grained, event-specific details.
• Score: 22.46313255627877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Localizing user-queried events through natural language is crucial for video understanding models. Recent methods predominantly adapt Video LLMs to generate event boundary timestamps to handle temporal localization tasks, which struggle to leverage LLMs' powerful semantic understanding. In this work, we introduce MeCo, a novel timestamp-free framework that enables video LLMs to fully harness their intrinsic semantic capabilities for temporal localization tasks. Rather than outputting boundary timestamps, MeCo partitions videos into holistic event and transition segments based on the proposed structural token generation and grounding pipeline, derived from video LLMs' temporal structure understanding capability. We further propose a query-focused captioning task that compels the LLM to extract fine-grained, event-specific details, bridging the gap between localization and higher-level semantics and enhancing localization performance. Extensive experiments on diverse temporal localization tasks show that MeCo consistently outperforms boundary-centric methods, underscoring the benefits of a semantic-driven approach for temporal localization with video LLMs.

Related papers

• Enrich and Detect: Video Temporal Grounding with Multimodal LLMs [60.224522472631776]
  We introduce ED-VTG, a method for fine-grained video temporal grounding utilizing multi-modal large language models.<n>Our approach harnesses the capabilities of multimodal LLMs to jointly process text and video.<n>We demonstrate state-of-the-art results across various benchmarks in temporal video grounding and paragraph grounding settings.
  arXiv  Detail & Related papers  (2025-10-19T22:12:45Z)
• TAG: A Simple Yet Effective Temporal-Aware Approach for Zero-Shot Video Temporal Grounding [12.57589714067771]
  Video Temporal Grounding aims to extract relevant video segments based on a given natural language query.<n>Previous approaches suffer from semantic fragmentation, where temporally continuous frames sharing the same semantics are split across multiple segments.<n>We propose a simple yet effective Temporal-Aware approach for zero-shot video temporal Grounding, which incorporates temporal pooling, temporal coherence clustering, and similarity adjustment.
  arXiv  Detail & Related papers  (2025-08-11T12:38:46Z)
• TSPO: Temporal Sampling Policy Optimization for Long-form Video Language Understanding [25.675553077419274]
  Multimodal Language Models (MLLMs) have demonstrated significant progress in vision tasks, yet they still face challenges when processing long-duration inputs.<n>We propose Temporal Sampling Policy Optimization (TSPO), advancing MLLMs' long-form video-language understanding via reinforcement learning.<n>Our TSPO state-of-the-art performance across multiple long video understanding benchmarks, and shows transferable ability across different cutting-edge Video-MLLMs.
  arXiv  Detail & Related papers  (2025-08-06T12:03:36Z)
• Do Language Models Understand Time? [2.290956583394892]
  Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and summarization.<n>This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities.<n>We analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations that constrain the temporal understanding of LLMs.
  arXiv  Detail & Related papers  (2024-12-18T13:38:06Z)
• MLLM as Video Narrator: Mitigating Modality Imbalance in Video Moment Retrieval [53.417646562344906]
  Video Moment Retrieval (VMR) aims to localize a specific temporal segment within an untrimmed long video given a natural language query. Existing methods often suffer from inadequate training annotations, i.e., the sentence typically matches with a fraction of the prominent video content in the foreground with limited wording diversity. This intrinsic modality imbalance leaves a considerable portion of visual information remaining unaligned with text. In this work, we take an MLLM as a video narrator to generate plausible textual descriptions of the video, thereby mitigating the modality imbalance and boosting the temporal localization.
  arXiv  Detail & Related papers  (2024-06-25T18:39:43Z)
• LITA: Language Instructed Temporal-Localization Assistant [71.68815100776278]
  We introduce time tokens that encode timestamps relative to the video length to better represent time in videos. We also introduce SlowFast tokens in the architecture to capture temporal information at fine temporal resolution. We show that our emphasis on temporal localization also substantially improves video-based text generation compared to existing Video LLMs.
  arXiv  Detail & Related papers  (2024-03-27T22:50:48Z)
• Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning [102.54669633984278]
  We propose Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks. We train Momentor on Moment-10M, enabling it to perform segment-level reasoning and localization.
  arXiv  Detail & Related papers  (2024-02-18T03:04:38Z)
• Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding [112.3913646778859]
  We propose a simple yet effective video-language modeling framework, S-ViLM. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features. S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T22:45:07Z)
• Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding [148.46348699343991]
  Temporal language grounding aims to localize a video segment in an untrimmed video based on a natural language description. Most of the existing weakly supervised methods generate a candidate segment set and learn cross-modal alignment through a MIL-based framework. We propose a novel candidate-free framework: Fine-grained Semantic Alignment Network (FSAN), for weakly supervised TLG.
  arXiv  Detail & Related papers  (2022-10-21T13:10:27Z)
• Learning to Combine the Modalities of Language and Video for Temporal Moment Localization [4.203274985072923]
  Temporal moment localization aims to retrieve the best video segment matching a moment specified by a query. We introduce a novel recurrent unit, cross-modal long short-term memory (CM-LSTM), by mimicking the human cognitive process of localizing temporal moments. We also devise a two-stream attention mechanism for both attended and unattended video features by the input query to prevent necessary visual information from being neglected.
  arXiv  Detail & Related papers  (2021-09-07T08:25:45Z)
• Progressive Localization Networks for Language-based Moment Localization [56.54450664871467]
  This paper focuses on the task of language-based moment localization. Most existing methods prefer to first sample sufficient candidate moments with various temporal lengths, and then match them with the given query to determine the target moment. We propose a novel multi-stage Progressive localization Network (PLN) which progressively localizes the target moment in a coarse-to-fine manner.
  arXiv  Detail & Related papers  (2021-02-02T03:45:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.