Weakly Supervised Video Moment Retrieval From Text Queries (1904.03282v2)

Abstract: There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue, in this work, we introduce the problem of learning from weak labels for the task of text to video moment retrieval. The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate. We propose a joint visual-semantic embedding based framework that learns the notion of relevant segments from video using only video-level sentence descriptions. Specifically, our main idea is to utilize latent alignment between video frames and sentence descriptions using Text-Guided Attention (TGA). TGA is then used during the test phase to retrieve relevant moments. Experiments on two benchmark datasets demonstrate that our method achieves comparable performance to state-of-the-art fully supervised approaches.

• The paper introduces a framework with Text-Guided Attention (TGA) to align video frames with sentence descriptions for efficient moment retrieval.
• It uses a two-branch network combining CNNs and GRUs to extract visual and textual features, achieving competitive results on benchmarks like Charades-STA and DiDeMo.
• The approach reduces reliance on labor-intensive temporal annotations, paving the way for scalable video analysis and future multimodal enhancements.

Weakly Supervised Video Moment Retrieval From Text Queries: A Comprehensive Overview

In the domain of text-based video moment retrieval, the paper by Mithun, Paul, and Roy-Chowdhury introduces an innovative approach to address the problem of weakly supervised video moment retrieval using text queries. Traditional methods typically require robust supervision with temporal boundary annotations, which can be labor-intensive and non-scalable. This research proposes a novel framework utilizing weak supervision through video-level sentence descriptions instead, thereby reducing the requirements for collection-intensive temporal annotation data.

Framework and Methodology

The core of the proposed solution lies in establishing a joint visual-semantic embedding framework that employs Text-Guided Attention (TGA). The framework ingeniously exploits video-text pairs during training to learn an alignment between video frames and full sentence descriptions. TGA is leveraged in the testing phase to accurately retrieve relevant video moments corresponding to given text queries. The paper illustrates how TGA can effectively highlight relevant temporal locations within a video using text descriptions as guidance.

The authors adopt a two-branch deep neural network architecture for feature extraction, where video frames and text descriptions are projected into a common embedding space. Visual features are derived using Convolutional Neural Networks (CNNs), while sentence descriptions are encoded using Gated Recurrent Units (GRUs). This configuration enables the model to maximize the semantic alignment between text and video content.

Experimental Validation

Experiments conducted on benchmark datasets, namely Charades-STA and DiDeMo, demonstrate that the proposed framework achieves performance comparable to some state-of-the-art fully supervised methods. Notably, the weakly supervised method presented in this paper showed competitive results with models that have the privilege of extensive annotated data. The comparison with supervised baselines such as CTRL and MCN highlights the efficacy of weak supervision for moment retrieval tasks.

Implications and Future Work

This research poses practical implications for efficiently scaling text-based video moment retrieval models by diminishing the reliance on fine-grained supervision. The approach unlocks the potential for leveraging vast amounts of video data available on the web, which typically includes video-level textual annotations rather than detailed temporal marking. As a natural evolution, future work may explore employing multimodal cues, including audio and context-enriched metadata, to refine attention mechanisms and enhance retrieval accuracy.

This paper not only advances the theoretical understanding of weakly supervised learning but also suggests promising avenues for improving AI-driven video analysis tools in highly diverse and expansive video datasets. With the challenges surrounding supervision in mind, the approach serves as a catalyst for advancing methods tailored to practical and efficient implementation in real-world applications.