Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos (1910.14303v1)

Abstract: Temporal sentence grounding in videos aims to detect and localize one target video segment, which semantically corresponds to a given sentence. Existing methods mainly tackle this task via matching and aligning semantics between a sentence and candidate video segments, while neglect the fact that the sentence information plays an important role in temporally correlating and composing the described contents in videos. In this paper, we propose a novel semantic conditioned dynamic modulation (SCDM) mechanism, which relies on the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence related video contents over time. More importantly, the proposed SCDM performs dynamically with respect to the diverse video contents so as to establish a more precise matching relationship between sentence and video, thereby improving the temporal grounding accuracy. Extensive experiments on three public datasets demonstrate that our proposed model outperforms the state-of-the-arts with clear margins, illustrating the ability of SCDM to better associate and localize relevant video contents for temporal sentence grounding. Our code for this paper is available at https://github.com/yytzsy/SCDM .

• The paper introduces SCDM, a novel method that dynamically modulates temporal convolutions using sentence semantics.
• It integrates multimodal fusion with semantic modulation to enhance alignment between video segments and textual queries.
• Evaluations on datasets like Charades-STA demonstrate superior performance, notably improving Recall@1 at an IoU threshold of 0.7.

Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos

This paper introduces a novel approach called Semantic Conditioned Dynamic Modulation (SCDM) to tackle the problem of Temporal Sentence Grounding in Videos (TSG). TSG is a challenging task that involves identifying the start and end timestamps of a video segment that semantically corresponds to a given natural language sentence. The video datasets are typically untrimmed, demanding precise segmentation that aligns video content with textual queries.

Existing methods for TSG often prioritize matching semantics between a sentence and potential video segments, while under-utilizing the influence of sentence semantics in guiding temporal correlation and composition of video content. To address this limitation, the paper proposes SCDM, which innovatively leverages sentence semantics to modulate temporal convolution operations within a hierarchical convolutional network. This modulation aligns sentence semantics with video content dynamically across diverse visual representations and temporal scales.

The architecture integrates multimodal fusion, semantic modulated temporal convolution, and a position prediction module. The multimodal fusion ensures initial interaction between sentence and video clips, producing a combined feature space. The semantic modulation within the convolutional network adjusts scaling and shifting parameters, thereby enhancing correlation of sentence-related video content. This modulation takes place dynamically, allowing the network to adapt to varying video contents and scales over time, and ultimately leading to more accurate TSG.

Empirical evaluations of the SCDM model on three datasets—TACoS, Charades-STA, and ActivityNet Captions—demonstrate its superiority over existing state-of-the-art methods. For instance, on the Charades-STA dataset, the SCDM model achieves a significant improvement with a Recall@1 and an IoU threshold of 0.7, underscoring its capability in handling complex temporal structures and achieving precise segmentations.

This research underscores the importance of semantic integration in video content analysis, marking a step forward in aligning visual and textual information dynamically. The SCDM model presents itself as a promising approach to enhance video understanding, particularly in applications requiring nuanced temporal segmentations influenced by linguistic cues.

The proposed model sets a foundation for future research, where such dynamic modulation techniques could be explored for finer-grained video analysis tasks, potentially integrating more advanced natural language processing techniques or exploring other types of modulations. The approach not only contributes to the field of video semantics but also bridges the interaction between vision and language more intuitively.

Overall, the methodology and results in this paper serve as a detailed exploration of sentence-varying semantic modulations, offering valuable insights for advancing TSG methodologies in AI research.