Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation

• URL: http://arxiv.org/abs/2107.11443v1
• Date: Fri, 23 Jul 2021 20:04:01 GMT
• Title: Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation
• Authors: Jiabo Huang, Yang Liu, Shaogang Gong and Hailin Jin
• Abstract summary: We develop a more accurate weakly-supervised solution by introducing Cross-Sentence Relations Mining. We explore two cross-sentence relational constraints: (1) trimmed ordering and (2) semantic consistency among sentences in a paragraph description of video activities. Experiments on two publicly available activity localisation datasets show the advantages of our approach over the state-of-the-art weakly supervised methods.
• Score: 79.50868197788773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video activity localisation has recently attained increasing attention due to its practical values in automatically localising the most salient visual segments corresponding to their language descriptions (sentences) from untrimmed and unstructured videos. For supervised model training, a temporal annotation of both the start and end time index of each video segment for a sentence (a video moment) must be given. This is not only very expensive but also sensitive to ambiguity and subjective annotation bias, a much harder task than image labelling. In this work, we develop a more accurate weakly-supervised solution by introducing Cross-Sentence Relations Mining (CRM) in video moment proposal generation and matching when only a paragraph description of activities without per-sentence temporal annotation is available. Specifically, we explore two cross-sentence relational constraints: (1) Temporal ordering and (2) semantic consistency among sentences in a paragraph description of video activities. Existing weakly-supervised techniques only consider within-sentence video segment correlations in training without considering cross-sentence paragraph context. This can mislead due to ambiguous expressions of individual sentences with visually indiscriminate video moment proposals in isolation. Experiments on two publicly available activity localisation datasets show the advantages of our approach over the state-of-the-art weakly supervised methods, especially so when the video activity descriptions become more complex.

Related papers

• 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)
• Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding [70.31050639330603]
  Video paragraph grounding aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. Existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. We introduce and explore Weakly-Supervised Video paragraph Grounding (WSVPG) to eliminate the need of temporal annotations.
  arXiv  Detail & Related papers  (2024-03-18T04:30:31Z)
• TempCLR: Temporal Alignment Representation with Contrastive Learning [35.12182087403215]
  We propose a contrastive learning framework TempCLR to compare the full video and the paragraph explicitly. In addition to pre-training on the video and paragraph, our approach can also generalize on the matching between video instances.
  arXiv  Detail & Related papers  (2022-12-28T08:10:31Z)
• Hierarchical Deep Residual Reasoning for Temporal Moment Localization [48.108468456043994]
  We propose a Hierarchical Deep Residual Reasoning (HDRR) model, which decomposes the video and sentence into multi-level representations with different semantics. We also design the simple yet effective Res-BiGRUs for feature fusion, which is able to grasp the useful information in a self-adapting manner.
  arXiv  Detail & Related papers  (2021-10-31T07:13:34Z)
• Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos [55.52369116870822]
  This paper focuses on tackling the problem of temporal language localization in videos. It aims to identify the start and end points of a moment described by a natural language sentence in an untrimmed video.
  arXiv  Detail & Related papers  (2021-10-12T14:59:25Z)
• Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos [63.94898634140878]
  Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. We propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. We evaluate the proposed method on three challenging public benchmarks: Ac-tivityNet Captions, TACoS, and Charades-STA.
  arXiv  Detail & Related papers  (2020-08-06T04:09:03Z)
• Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video [53.69956349097428]
  Given an untrimmed video and a query sentence, our goal is to localize a temporal segment in the video that semantically corresponds to the query sentence. We propose a two-stage model to tackle this problem in a coarse-to-fine manner.
  arXiv  Detail & Related papers  (2020-01-25T13:07:43Z)

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.