Grounded Video Description (1812.06587v2)

Abstract: Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or "true" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video.

• The paper introduces a grounded video description approach and the ActivityNet-Entities dataset with 158,000 bounding box annotations to address ungrounded descriptions in existing models.
• Their novel model employs spatial and temporal self-attention with attention, grounding, and classification supervision to link descriptions directly to visual content.
• The model achieves state-of-the-art performance on video description tasks, demonstrating improved grounding and language metrics (e.g., CIDEr 47.5 on ActivityNet-Entities) and highlighting the importance of explicit visual grounding for explainability.

Grounded Video Description: An Expert Overview

The paper "Grounded Video Description" by Zhou et al. introduces a significant advancement in the domain of vision and language understanding, particularly focusing on video description tasks. The authors address a prevalent issue observed in video description models: the generation of descriptions that are not truly grounded in the video content but rather influenced by prior knowledge ingrained during training. This can lead to hallucinations, where models inaccurately include objects not present in the video.

A core contribution of this work is the introduction of the ActivityNet-Entities dataset. This novel dataset is an augmentation of the ActivityNet Captions dataset and includes approximately 158,000 bounding box annotations, each corresponding to noun phrases in the video descriptions. This extensive annotation enables the development and evaluation of video description models with a strong grounding component. The dataset aids in assessing the realism of generated descriptions and provides a benchmark for the degree to which models refer explicitly to the visual content, thus questioning the groundedness of model outputs.

The authors propose a new video description model that utilizes these bounding box annotations to produce more accurately grounded descriptions. This model achieves state-of-the-art (SotA) performance not only on video description tasks but also shows its applicability to image description tasks, specifically on the Flickr30k Entities dataset. The experimental results indicate that this grounded model outperforms existing methods in grounding accuracy, thus enhancing the model's reliability and explainability.

From a methodological standpoint, the grounding-based video description model integrates a self-attention mechanism to capture contextual information across different video regions. This integrates both spatial and temporal attention mechanisms, allowing the model to dynamically focus on important video frames and object regions. Such integration enhances the model's ability to generate descriptions that are tightly aligned with the video content. Notably, this model employs three types of supervision—attention, grounding, and classification—to ensure the generated descriptions are well-aligned with visual evidence. The results show marked improvements over unsupervised approaches, with substantial gains in grounding accuracy, indicating that explicit supervision can lead to better alignment between generated content and video input.

The numerical results presented in the paper highlight the effectiveness of the proposed method. On the ActivityNet-Entities validation set, the grounded model demonstrates an attention localization accuracy of 35.7% and grounding localization accuracy of 44.9%. Moreover, the model shows a noticeable improvement in language evaluation metrics, achieving a CIDEr score of 47.5. Similarly, when applied to the Flickr30k Entities dataset, the model achieves a CIDEr score of 62.3, asserting its superiority in both video and image description domains.

This research has profound implications for future developments in AI, particularly in enhancing the transparency and accountability of AI systems that generate natural language descriptions from visual inputs. The ability for these models to provide grounded and explainable descriptions is crucial for applications where AI assists users, such as aiding visually impaired individuals. Furthermore, the methodological advancements and datasets introduced by Zhou et al. have the potential to spur future research into the development of more sophisticated and context-aware AI systems, advancing both theoretical understanding and practical capabilities in vision-language integration. The ActivityNet-Entities dataset, along with the proposed model, will serve as a critical baseline for evaluating future innovations in this space.