Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning (1804.00100v2)

Abstract: Dense video captioning is a newly emerging task that aims at both localizing and describing all events in a video. We identify and tackle two challenges on this task, namely, (1) how to utilize both past and future contexts for accurate event proposal predictions, and (2) how to construct informative input to the decoder for generating natural event descriptions. First, previous works predominantly generate temporal event proposals in the forward direction, which neglects future video context. We propose a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions. Second, different events ending at (nearly) the same time are indistinguishable in the previous works, resulting in the same captions. We solve this problem by representing each event with an attentive fusion of hidden states from the proposal module and video contents (e.g., C3D features). We further propose a novel context gating mechanism to balance the contributions from the current event and its surrounding contexts dynamically. We empirically show that our attentively fused event representation is superior to the proposal hidden states or video contents alone. By coupling proposal and captioning modules into one unified framework, our model outperforms the state-of-the-arts on the ActivityNet Captions dataset with a relative gain of over 100% (Meteor score increases from 4.82 to 9.65).

Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning

The paper "Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning," presents a significant advancement in dense video captioning. Dense video captioning involves both localizing and describing all events within a video, which presents distinct challenges compared to video captioning of shorter clips. The authors address two primary challenges in this domain: effectively utilizing both past and future contexts for accurate event proposal predictions, and constructing informative input to the decoder for generating natural event descriptions.

Methodology and Contributions

The authors propose a novel bidirectional proposal mechanism, termed Bidirectional Single Stream Temporal Proposals (Bidirectional SST), which employs both forward and backward passes over the video sequence. This approach allows the model to incorporate both past and future contexts when proposing temporal events. In the forward pass, the model processes the video in the forward direction to capture past and current event information, while in the backward pass, it processes the video in reverse to capture future and current event information. By merging these insights, the model significantly improves the quality of event proposal predictions.

To further enhance the representation of detected event proposals for the captioning task, the authors introduce a fusion strategy that combines the contextual information represented by hidden states at the event's boundaries with the visual content of the event itself (e.g., C3D features). This method, termed attentive fusion, dynamically balances the contributions of the event content and its context, leveraging a context gating mechanism. Context gating explicitly modulates the influence of current events and surrounding contexts in generating the event's description. This sophisticated combination results in a more discriminative and informative representation of events, addressing the challenge of distinguishing between overlapping events that might otherwise generate similar captions.

Results and Implications

The empirical results demonstrate that the proposed Bidirectional SST method provides a superior framework for both the task of event localization and dense captioning. The proposed model outperforms state-of-the-art methods on the challenging ActivityNet Captions dataset, achieving a substantial improvement in the Meteor score from 4.82 to 9.65. This relative gain of over 100% underscores the efficacy of the bidirectional approach and the attentive fusion strategy in effectively capturing and representing both visual and temporal information within videos.

The contributions of this research have theoretical and practical implications. The integration of both forward and backward context in proposal generation enhances the understanding and localization of complex events in lengthy videos—a challenge that has stymied previous efforts. Additionally, the context gating mechanism and attentive fusion offer new pathways for future research in video understanding and natural language generation by emphasizing the dynamic interaction between event-specific features and their temporal contexts.

In conclusion, the paper provides meaningful advancements in dense video captioning by deploying a novel bidirectional attentive fusion with context gating framework. These methodological innovations show promise for improving automated video analysis applications, which have become increasingly vital with the explosive growth of video content on the internet. Future developments in AI, drawing from insights in this paper, are likely to explore enhanced integration of multimodal data and contextual reasoning for more accurate and contextually rich video understanding.