Joint Video and Text Parsing for Understanding Events and Answering Queries

Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe and Song-Chun Zhu

Abstract

We propose a framework for parsing video and text jointly, generating narrative text descriptions and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results.

Examples

Example 1: (a) An example surveillance video and the text descriptions of a parking lot scene. (b) The video parse graph, text parse graph and joint parse graph. The video and text parses have significant overlap. In the joint parse graph, the green shadow denotes the subgraph from the video and the blue shadow denotes the subgraph from the text.

Example 2: (a) An example surveillance video and the text descriptions of a courtyard scene. (b) The video parse graph, text parse graph and joint parse graph. The video and text parses have no overlap. In the joint parse graph, the green shadow denotes the subgraph from the video and the blue shadow denotes the subgraph from the text.

Demo

Publication

Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe and Song-Chun Zhu, "Joint Video and Text Parsing for Understanding Events and Answering Queries". In IEEE MultiMedia, vol. 21, no. 2, pp. 42-70, 2014.(arXiv)

The work is supported by the DARPA grant FA 8650-11-1-7149, the ONR grant N000141010933 and the NSF CDI grant CNS 1028381.