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Jointly Localizing and Describing Events for Dense Video Captioning
Yehao Li, Ting Yao, Yingwei Pan, Hongyang Chao, Tao Mei
TL;DR
Dense video captioning must localize and describe multiple events, a challenge that two-stage proposal and captioning methods may address sub-optimally. The paper unifies temporal event proposals and sentence generation end to end through descriptiveness regression in a single-shot detector. On ActivityNet Captions, the framework improves over state-of-the-art techniques and achieves a METEOR score of 12.96% on the official test set.
Problem
Dense video captioning requires accurately localizing each event in time and generating a sentence for it, while two-stage methods may destroy their interaction.
Method
The framework jointly and globally optimizes detection and captioning end to end by using descriptiveness regression as a bridge between temporal proposal adjustment and sentence generation.
Results
12.96% METEOR was achieved on the ActivityNet Captions official testing set, ranking first on its leaderboard.
Takeaways & Limitations
The architecture provides a unified approach that integrates language-based descriptiveness into temporal event proposal learning and dense video captioning.
Abstract
from arXiv · showhide
Automatically describing a video with natural language is regarded as a fundamental challenge in computer vision. The problem nevertheless is not trivial especially when a video contains multiple events to be worthy of mention, which often happens in real videos. A valid question is how to temporally localize and then describe events, which is known as "dense video captioning." In this paper, we present a novel framework for dense video captioning that unifies the localization of temporal event proposals and sentence generation of each proposal, by jointly training them in an end-to-end manner. To combine these two worlds, we integrate a new design, namely descriptiveness regression, into a single shot detection structure to infer the descriptive complexity of each detected proposal via sentence generation. This in turn adjusts the temporal locations of each event proposal. Our model differs from existing dense video captioning methods since we propose a joint and global optimization of detection and captioning, and the framework uniquely capitalizes on an attribute-augmented video captioning architecture. Extensive experiments are conducted on ActivityNet Captions dataset and our framework shows clear improvements when compared to the state-of-the-art techniques. More remarkably, we obtain a new record: METEOR of 12.96% on ActivityNet Captions official test set.
1. Introduction
Dense video captioning addresses the need to localize and describe multiple events in long, real-world videos. This paper proposes an architecture that jointly optimizes temporal event localization and sentence generation through descriptiveness regression.
- Motivation: Conventional video captioning often generates one sentence, which cannot recapitulate all events in long videos containing multiple activities.The paper illustrates this limitation with a video of playing frisbee with a dog, whose procedure is not described in detail by a single caption.
- Motivation: Dense video captioning must solve both accurate temporal event localization and powerful sentence generation.Prior work addresses temporal action proposals and video captioning as separate aspects.
- Motivation: A two-stage proposal-then-caption pipeline may destroy interaction between localization and description, producing a sub-optimal solution.The paper identifies this interaction as a central challenge in dense video captioning.
- Contribution: Descriptiveness regression measures each event’s descriptive complexity and adjusts its temporal proposal within a single-shot detection structure.It serves as a bridge between language-based description and temporal event proposal learning.
- Contribution: The proposed architecture unifies temporal event localization with sentence generation in a single dense video captioning framework.This interaction is presented as the paper’s main architectural contribution.
2. Related Work
Prior work develops temporal action proposal methods and video captioning methods largely as separate research directions. The paper positions its approach as enhancing both event proposals and sentence generation through language-aware temporal modeling and semantic attributes.
- Temporal Action Proposal: Temporal action proposal research detects action-containing segments using sliding windows, classifiers, dictionary learning, or 3D CNNs.These methods include class-independent proposal retrieval and temporal coordinate regression.
- Video Captioning: Video captioning research includes template-based methods and sequence-learning approaches that combine CNNs with RNNs.Template methods use detected keywords, whereas sequence models generate more flexible syntactical structures.
- Summary: This work enhances temporal proposals with both boundary regression and descriptiveness regression rather than only event/background classification.Descriptiveness regression estimates whether an event can be well described from a language perspective.
- Summary: The sentence generation module is further augmented with semantic attributes and reinforcement learning to optimize LSTM-based captioning with non-differentiable metrics.These additions complement the proposal enhancements in the paper’s dense video captioning framework.
3. Dense Video Captioning
The framework jointly localizes temporal event proposals and generates captions, using descriptiveness to connect detection with language generation. It combines single-shot proposal detection, attribute-augmented captioning, and end-to-end optimization.
- Dense video captioning produces temporally localized descriptions for multiple events rather than one caption for an entire video.
- The TEP module jointly predicts eventness, refines temporal boundaries, and estimates each proposal’s descriptive complexity.These functions are integrated into one network and single-shot detection architecture.
- Candidate proposals are ranked using fused eventness and descriptiveness scores before proposals above a confidence threshold enter sentence generation.
- The SG module encodes event features and detected attributes with an attribute-augmented LSTM and optimizes generation with sentence-level reinforcement learning rewards.The reward compares generated sentences with ground-truth sentences using a non-differentiable evaluation metric.
- Descriptiveness scores also provide temporal attention that weights clips by their usefulness for sentence generation when aggregating proposal features.More descriptive clips receive higher attention weights in the aggregated proposal representation.
- The overall objective jointly optimizes TEP and SG, enabling end-to-end interaction between detection and captioning.Descriptiveness acts as a bridge by influencing proposal adjustment and sentence-generation attention.
4. Experiments
Experiments on ActivityNet Captions evaluate DVC for dense video captioning and temporal event proposal tasks, comparing it with established captioning and proposal baselines. DVC achieves stronger benchmark performance and improves proposal quality through descriptiveness regression.
- Experimental Setup: Experiments use ActivityNet Captions to evaluate dense video captioning and temporal event proposal performance.The dataset contains 20,000 videos with temporally annotated sentences, and the study follows train, validation, and test splits.
- Experimental Setup: DVC is compared with LSTM, S2VT, TA, H-RNN, and DCE, using both ground-truth and learnt temporal proposals.DVC-D and DVC-D-A provide variants incorporating descriptiveness-driven temporal attention, attributes, and alternative optimization choices.
- Dense Video Captioning Task: DVC generates more relevant and descriptive event sentences than LSTM in qualitative examples.One example changes “brushing the dog’s hair” to the more precise description “cutting the cat’s claws.”
- Dense Video Captioning Task: 10.33% METEOR is achieved by DVC with ground-truth proposals, a 16.33% relative improvement over DCE.The validation comparison reports consistent superiority across six evaluation metrics against the competing captioning techniques.
- Dense Video Captioning Task: 12.96% METEOR ranks DVC first on the official ActivityNet testing leaderboard when using P3D ResNet clip features.The C3D submission also achieves the best METEOR score among the compared top-performing methods.
- Temporal Event Proposal Task: DVC outperforms other methods on the temporal proposal AUC metric, with descriptiveness regression further improving DCE and TURN.The result supports refining event proposals from the language perspective through joint detection and captioning.
5. Conclusions
The paper presents a deep architecture that unifies temporal event localization and sentence generation for dense video captioning. Experiments on ActivityNet Captions validate the framework and report superior results on both dense video captioning and temporal event proposal tasks.
- The architecture unifies temporal localization of event proposals and sentence generation for dense video captioning.
- Descriptiveness regression bridges the two subtasks by adjusting event proposals from language information and measuring each event’s descriptive complexity.
- Experiments on ActivityNet Captions validate the model and its analysis.
- The framework achieves superior results over state-of-the-art methods on both dense video captioning and temporal event proposal tasks.