Temporal action proposal generation is an essential and challenging
task that aims at localizing temporal intervals containing
human actions in untrimmed videos. Most of existing
approaches are unable to follow the human cognitive process
of understanding the video context due to lack of attention
mechanism to express the concept of an action or an agent
who performs the action or the interaction between the agent
and the environment. Based on the action definition that a human,
known as an agent, interacts with the environment and
performs an action that affects the environment, we propose
a contextual Agent-Environment Network. Our proposed
contextual AEN involves (i) agent pathway, operating at a
local level to tell about which humans/agents are acting and
(ii) environment pathway operating at a global level to tell
about how the agents interact with the environment. Comprehensive
evaluations on 20-action THUMOS-14 and 200-
action ActivityNet-1.3 datasets with different backbone networks,
i.e C3D and SlowFast, show that our method robustly
exhibits outperformance against state-of-the-art methods regardless
of the employed backbone network.
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Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent{'}s actions can bring about myriad changes in the scene. Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional video captioning. Unlike images, actions in videos are also inherently linked to social aspects such as intentions (why the action is taking place), effects (what changes due to the action), and attributes that describe the agent. Thus for video understanding, such as when captioning videos or when answering questions about videos, one must have an understanding of these commonsense aspects. We present the first work on generating \textit{commonsense} captions directly from videos, to describe latent aspects such as intentions, effects, and attributes. We present a new dataset {``}Video-to-Commonsense (V2C){''} that contains {\textasciitilde}9k videos of human agents performing various actions, annotated with 3 types of commonsense descriptions. Additionally we explore the use of open-ended video-based commonsense question answering (V2C-QA) as a way to enrich our captions. Both the generation task and the QA task can be used to enrich video captions.
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- Award ID(s):
- 1816039
- PAR ID:
- 10276932
- Date Published:
- Journal Name:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Page Range / eLocation ID:
- 840 to 860
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Video Paragraph Captioning aims to generate a multi-sentence description of an untrimmed video with multiple temporal event locations in a coherent storytelling. Following the human perception process, where the scene is effectively understood by decomposing it into visual (e.g. human, animal) and non- visual components (e.g. action, relations) under the mutual influence of vision and language, we first propose a visual- linguistic (VL) feature. In the proposed VL feature, the scene is modeled by three modalities including (i) a global visual environment; (ii) local visual main agents; (iii) linguistic scene elements. We then introduce an autoregressive Transformer- in-Transformer (TinT) to simultaneously capture the semantic coherence of intra- and inter-event contents within a video. Finally, we present a new VL contrastive loss function to guarantee the learnt embedding features are consistent with the captions semantics. Comprehensive experiments and extensive ablation studies on the ActivityNet Captions and YouCookII datasets show that the proposed Visual-Linguistic Transformer- in-Transform (VLTinT) outperforms previous state-of-the-art methods in terms of accuracy and diversity. The source code is made publicly available at: https://github.com/UARK-AICV/ VLTinT.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2020.emnlp-main.61
