This paper focuses on semantic task planning, i.e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The primary challenges are how to model task-specific knowledge and how to integrate this knowledge into the learning procedure. In this work, we propose training a recurrent long short-term memory (LSTM) network to address this problem, i.e., taking a scene image (including pre-located objects) and the specified task as input and recurrently predicting action sequences. However, training such a network generally requires large numbers of annotated samples to cover the semantic space (e.g., diverse action decomposition and ordering). To overcome this issue, we introduce a knowledge and-or graph (AOG) for task description, which hierarchically represents a task as atomic actions. With this AOG representation, we can produce many valid samples (i.e., action sequences according to common sense) by training another auxiliary LSTM network with a small set of annotated samples. Furthermore, these generated samples (i.e., task-oriented action sequences) effectively facilitate training of the model for semantic task planning. In our experiments, we create a new dataset that contains diverse daily tasks and extensively evaluate the effectiveness of our approach.
RNNs @cite_73 , particularly LSTM @cite_4 , were developed for modeling long-term temporal dependencies. Recently, RNNs have been extensively applied to various sequence prediction tasks, including natural language generation @cite_23 @cite_25 @cite_10 , neural machine translation @cite_2 @cite_70 @cite_69 @cite_58 , and image and video captioning @cite_12 @cite_68 @cite_33 @cite_38 @cite_67 . These works adopted a similar encoder-decoder architecture for solving sequence prediction. @cite_10 encoded the contexts into a continuous semantic representation and then decoded the semantic representation into context-aware text sequences using RNNs. @cite_2 mapped a free-form source language sentence into the target language by utilizing the encoder-decoder recurrent network. @cite_12 applied a similar pipeline for image captioning, therein employing a CNN as the encoder to extract image features and an LSTM network as the decoder to generate the descriptive sentence. @cite_38 further adapted this pipeline to video caption generation by developing a hierarchical recurrent neural encoder that is capable of efficiently exploiting the video temporal structure in a longer range to extract video representations.
@cite_38 @cite_69 @cite_67 @cite_4 @cite_33 @cite_70 @cite_58 @cite_68 @cite_23 @cite_2 @cite_73 @cite_10 @cite_25 @cite_12
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Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community. Most recent progress in this problem has been achieved through employing 2-D and or 3-D Convolutional Neural Networks (CNNs) to encode video content and Recurrent Neural Networks (RNNs) to decode a sentence. In this paper, we present Long Short-Term Memory with Transferred Semantic Attributes (LSTM-TSA)&#x2014;a novel deep architecture that incorporates the transferred semantic attributes learnt from images and videos into the CNN plus RNN framework, by training them in an end-to-end manner. The design of LSTM-TSA is highly inspired by the facts that 1) semantic attributes play a significant contribution to captioning, and 2) images and videos carry complementary semantics and thus can reinforce each other for captioning. To boost video captioning, we propose a novel transfer unit to model the mutually correlated attributes learnt from images and videos. Extensive experiments are conducted on three public datasets, i.e., MSVD, M-VAD and MPII-MD. Our proposed LSTM-TSA achieves to-date the best published performance in sentence generation on MSVD: 52.8 and 74.0 in terms of BLEU@4 and CIDEr-D. Superior results are also reported on M-VAD and MPII-MD when compared to state-of-the-art methods. Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community. Most recent progress in this problem has been achieved through employing 2-D and or 3-D Convolutional Neural Networks (CNN) to encode video content and Recurrent Neural Networks (RNN) to decode a sentence. In this paper, we present Long Short-Term Memory with Transferred Semantic Attributes (LSTM-TSA)---a novel deep architecture that incorporates the transferred semantic attributes learnt from images and videos into the CNN plus RNN framework, by training them in an end-to-end manner. The design of LSTM-TSA is highly inspired by the facts that 1) semantic attributes play a significant contribution to captioning, and 2) images and videos carry complementary semantics and thus can reinforce each other for captioning. To boost video captioning, we propose a novel transfer unit to model the mutually correlated attributes learnt from images and videos. Extensive experiments are conducted on three public datasets, i.e., MSVD, M-VAD and MPII-MD. Our proposed LSTM-TSA achieves to-date the best published performance in sentence generation on MSVD: 52.8 and 74.0 in terms of BLEU@4 and CIDEr-D. Superior results when compared to state-of-the-art methods are also reported on M-VAD and MPII-MD. Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term Memory (LSTM), have been widely used for sequence labeling. In this paper, we first enhance LSTM-based sequence labeling to explicitly model label dependencies. Then we propose another enhancement to incorporate the global information spanning over the whole input sequence. The latter proposed method, encoder-labeler LSTM, first encodes the whole input sequence into a fixed length vector with the encoder LSTM, and then uses this encoded vector as the initial state of another LSTM for sequence labeling. Combining these methods, we can predict the label sequence with considering label dependencies and information of whole input sequence. In the experiments of a slot filling task, which is an essential component of natural language understanding, with using the standard ATIS corpus, we achieved the state-of-the-art F1-score of 95.66 . Recent progress in using recurrent neural networks (RNNs) for image description has motivated us to explore the application of RNNs to video description. Recent work has also suggested that attention mechanisms may be able to increase performance. To this end, we apply a long short-term memory (LSTM) network in two configurations: with a recently introduced soft-attention mechanism, and without. Our results suggest two things. First, incorporating a soft-attention mechanism into the text generation RNN significantly improves the quality of the descriptions. Second, using a combination of still frame features and dynamic motion-based features can also help. Ultimately, our combined approach exceeds the state-of-art on both BLEU and Meteor on the Youtube2Text dataset. We also present results on a new, larger and more complex dataset of paired video and natural language descriptions based on the use of Descriptive Video Service (DVS) annotations which are now widely available as an additional audio track on many DVDs.