Boundary Proposal Network for Two-Stage Natural Language Video Localization

Recently, a task of Natural Language Video Localization has been proposed. Given an untrimmed video and a natural language query, the task localizes the video segment relevant to the query by determining the start point and the end point. A new paper proposes a two-stage end-to-end frame-work termed Boundary Proposal Network. It inherits the merits of past methods and avoids their defects.

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Firstly, several high-quality segment proposals are generated. Then, an individual classifier matches the proposals with the sentence by predicting the matching score. The approach is adaptable to video segments of arbitrary length. The segment-level video feature and query feature are modeled jointly to boost the performance.

The proposed network outperforms the state-of-the-art approaches on three benchmark datasets. Moreover, it is a universal paradigm where can be individual modules can be replaced by other effective methods.

We aim to address the problem of Natural Language Video Localization (NLVL)-localizing the video segment corresponding to a natural language description in a long and untrimmed video. State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e.g., by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame as a boundary or intermediate frame inside the positive segment. However, both kinds of one-stage approaches have inherent drawbacks: the anchor-based approach is susceptible to the heuristic rules, further limiting the capability of handling videos with variant length. While the anchor-free approach fails to exploit the segment-level interaction thus achieving inferior results. In this paper, we propose a novel Boundary Proposal Network (BPNet), a universal two-stage framework that gets rid of the issues mentioned above. Specifically, in the first stage, BPNet utilizes an anchor-free model to generate a group of high-quality candidate video segments with their boundaries. In the second stage, a visual-language fusion layer is proposed to jointly model the multi-modal interaction between the candidate and the language query, followed by a matching score rating layer that outputs the alignment score for each candidate. We evaluate our BPNet on three challenging NLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive experiments and ablative studies on these datasets demonstrate that the BPNet outperforms the state-of-the-art methods.

Research paper: Xiao, S., “Boundary Proposal Network for Two-Stage Natural Language Video Localization”, 2021. Link:


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