Video summarization aims to select key frames/shots among videos to summarize the main storyline and has been widely investigated for facilitating video understanding [Plummer et al.(2017)Plummer, Brown, and Lazebnik, Mahasseni et al.(2017)Mahasseni, Lam, and Todorovic, Zhou and Qiao(2017), Yuan et al.(2017)Yuan, Liang, Wang, Yeung, and Gupta, Han et al.(2018)Han, Yang, Zhang, Chang, and Liang, Goyal et al.(2017)Goyal, Hu, Liang, Wang, and Xing]. As shown in Figure 1
, this task can be classified into two types: a) generic video summarization, which only takes the visual features of the video contents as the input and b) query-conditioned video summarization which conditions summarization on user queries.
The generic video summarization task has been addressed at three different levels: shot-level [Lin et al.(2015)Lin, Morariu, and Hsu, Lu and Grauman(2013)], frame-level [Kim et al.(2014)Kim, Sigal, and Xing, Khosla et al.(2013)Khosla, Hamid, Lin, and Sundaresan], and object-level [Meng et al.(2016)Meng, Wang, Yuan, and Tan, Zhang et al.(2018b)Zhang, Liang, Zhang, Tan, and Xing]
video summarization by selecting key shots/frames/objects in the videos. However, one main issue with generic video summarization is the fact that it does not take user preferences into account, since different users may have different preferences towards the video content, and a single evaluation metric is not robust enough for all video summaries[Sharghi et al.(2017)Sharghi, Laurel, and Gong].
Recently, another research direction, query-conditioned video summarization [Sharghi et al.(2016)Sharghi, Gong, and Shah, Vasudevan et al.(2017)Vasudevan, Gygli, Volokitin, and Van Gool, Sharghi et al.(2017)Sharghi, Laurel, and Gong], has been explored, which takes advantage of different user queries in form of texts to learn more user-oriented summaries. It generates user-oriented summaries that have effective correlations between summaries and query, and capture the overall essence of the video. Several approaches to query-conditioned video summarization have been proposed. Sharghi et al. [Sharghi et al.(2016)Sharghi, Gong, and Shah] first extend a sequential DPP (seqDPP) [Gong et al.(2014)Gong, Chao, Grauman, and Sha] to extract key shots. Afterwards, they develop a more comprehensive dataset for this task, and propose a memory network [Sukhbaatar et al.(2015)Sukhbaatar, Weston, Fergus, et al.] parameterized seqDPP model. However, there is still room to learn a better summarizer due to the limitation of the memory to jointly encode video and query.
To address the above issue we develop a query-conditioned three-player generative adversarial network architecture. We encode the query and the video sequence to learn a joint representation combining visual and text information, and take this query-conditioned representation as the input to the generative adversarial network. A three-player structure is applied during joint training, in order to achieve superior regularization. The contribution in our work can be summarized as follows: first, we propose a query-conditioned three-player adversarial network, which jointly encodes query and visual information and learns in an adversarial manner. Second, we introduce a three-player structure for the adversarial training. The discriminator regularizes the model via the three-player loss, which facilitates the generator to generate more related and meaningful video summaries. Two supervised losses are applied to ensure a more compact summary. One loss regularizes the length and the other aligns prediction and ground truth. Experimental results on a public dataset [Sharghi et al.(2017)Sharghi, Laurel, and Gong] demonstrate the superiority of our proposed approach against the state-of-the-art method.
2 Related Work
2.1 Generic Video Summarization
Generic video summarization [Kim et al.(2014)Kim, Sigal, and Xing, Khosla et al.(2013)Khosla, Hamid, Lin, and Sundaresan, Zhang et al.(2016a)Zhang, Chao, Sha, and Grauman, Zhang et al.(2016b)Zhang, Chao, Sha, and Grauman, Lin et al.(2015)Lin, Morariu, and Hsu], has been widely studied for efficient video analysis and video understanding. For shot-level video summarization [Lu and Grauman(2013), Yao et al.(2016)Yao, Mei, and Rui, Song et al.(2015)Song, Vallmitjana, Stent, and Jaimes, Lin et al.(2015)Lin, Morariu, and Hsu], Song et al. [Song et al.(2015)Song, Vallmitjana, Stent, and Jaimes] propose to learn the canonical visual concepts which are shared between videos and images to find important shots. In [Yao et al.(2016)Yao, Mei, and Rui], a pairwise deep ranking model is proposed to distinguish highlight segments from non-highlight ones. For frame-level video summarization [Khosla et al.(2013)Khosla, Hamid, Lin, and Sundaresan, Kim et al.(2014)Kim, Sigal, and Xing, Gong et al.(2014)Gong, Chao, Grauman, and Sha, Zhang et al.(2018a)Zhang, Kampffmeyer, Liang, Zhang, Tan, and Xing], Khosla et al. [Khosla et al.(2013)Khosla, Hamid, Lin, and Sundaresan] use web-images as a prior to facilitate video summarization. In [Gong et al.(2014)Gong, Chao, Grauman, and Sha], a probabilistic model is proposed for learning sequential structures to generate summaries. Approaches to object-level video summarization [Meng et al.(2016)Meng, Wang, Yuan, and Tan, Zhang et al.(2018b)Zhang, Liang, Zhang, Tan, and Xing] aim to obtain representative objects to perform fine-grained summarization. Currently there are two existing GAN-based works [Mahasseni et al.(2017)Mahasseni, Lam, and Todorovic, Zhang et al.(2018a)Zhang, Kampffmeyer, Liang, Zhang, Tan, and Xing] that include regularization using adversarial training. However, they do not consider user preferences, so the summaries may not be robust and may not generalize well to different users. Therefore, we investigate the query-conditioned video summarization task to provide more personalized summarization results by relying on user queries.
2.2 Query-conditioned Video Summarization
Query-conditioned video summarization [Sharghi et al.(2016)Sharghi, Gong, and Shah, Sharghi et al.(2017)Sharghi, Laurel, and Gong, Vasudevan et al.(2017)Vasudevan, Gygli, Volokitin, and Van Gool, Oosterhuis et al.(2016)Oosterhuis, Ravi, and Bendersky, Ji et al.(2017)Ji, Ma, Pang, and Li] takes user queries in the form of texts into consideration in order to learn more user-oriented summaries. In [Sharghi et al.(2016)Sharghi, Gong, and Shah], a Sequential and Hierarchical DPP (SH-DPP) is developed to tackle this challenge. In [Vasudevan et al.(2017)Vasudevan, Gygli, Volokitin, and Van Gool], the authors adopt a quality-aware relevance model and submodular mixtures to pick relevant and representative frames. There are two other works related to query-conditioned video summarization. One is used to generate visual trailers, while the other obtains web images conditioned on user queries, and then produces video summaries from both images and videos. Specifically, Oosterhuis et al. [Oosterhuis et al.(2016)Oosterhuis, Ravi, and Bendersky] propose a graph-based method to generate visual trailers by selecting frames that are most relevant to a given user’s query. Ji et al. [Ji et al.(2017)Ji, Ma, Pang, and Li] formulate the task by incorporating web images, which are obtained from user query searches. Thus the video summarization is indirectly conditioned on the query through the web images.
Recently, Sharghi et al. [Sharghi et al.(2017)Sharghi, Laurel, and Gong]
explore a more thoroughly query-conditioned video summarization approach. Instead of using datasets which are originally collected for the generic task, they propose a new dataset and an evaluation metric towards this task. Our work is developed based on this new dataset and the evaluation metric. We propose a novel query-conditioned adversarial network which does not rely on external knowledge, such as web images, and can effectively summarize videos based on user queries by integrating a three-player adversarial training structure.
3 Proposed Algorithm
3.1 Generator Network
Our proposed network facilitates query-conditioned video summarization by applying an adversarial network that takes the query into consideration with a three-player discriminator loss. Figure 2 illustrates the whole framework of our approach. The generator is mainly tasked with embedding visual information and text jointly, in order to provide comprehensive query-conditioned representations. The discriminator aims to distinguish the real summaries, i.e., ground-truth summary from random and generated summaries.
In the following sections, we first introduce the query-conditioned generator network in our model to select key shots with regards to different queries. We then present the proposed query-conditioned discriminator with three-player loss, which distinguishes the ground-truth summary from the random and the generated summaries. Finally, we introduce the details of adversarial training with two supervision losses in our model.
3.1.1 Query-Conditioned Feature Representation Module
Frame-level visual representation.
We denote an input video as , where denotes the total number of shots in a video. Each video shot is partitioned into 75 frames (5-second long) for fair comparison with related work [Sharghi et al.(2017)Sharghi, Laurel, and Gong]. As shown in Figure 2, the model aims to generate feature representations which are conditioned on user queries. We first apply the ResNet-152 feature extractor [He et al.(2016)He, Zhang, Ren, and Sun] to encode frame-level visual features. In order to do this, we downsample each shot to 16 frames per segment. The “fc7” layer of the ResNet-152 model trained on the ILSVRC 2015 dataset [Russakovsky et al.(2015)Russakovsky, Deng, Su, Krause, Satheesh, Ma, Huang, Karpathy, Khosla, Bernstein, et al.]
is used to obtain features for frames within each shot and followed by an average pooling layer. This frame-level feature vector is denoted as.
Shot-level visual representation.
We apply the C3D video descriptor [Tran et al.(2015)Tran, Bourdev, Fergus, Torresani, and Paluri], a network trained on the Sports1M dataset [Tran et al.(2015)Tran, Bourdev, Fergus, Torresani, and Paluri], to extract shot-level feature representation. We use the output of the “fc6” layer of the C3D and uniformly split the video into 75 frames before downsampling it to 16 frames per segment, aligning it with the extracted ResNet features, to get the shot-level visual features. The features we extracted from the C3D are denoted as .
To obtain textual feature representations, we use the Skip-gram model [Mikolov et al.(2013)Mikolov, Sutskever, Chen, Corrado, and Dean], a word2vec model pretrained on the GoogleNews dataset, to project each word into a semantic feature space. We define each user query as . Each contains two concepts (words), and we generate the concept embedding by summing up the two feature vectors of the two concepts. After that, we encode the textual representation by applying a fully connected layer.
Query-Conditioned Feature representation.
We first combine frame-level and shot-level feature vectors and by concatenation, followed by a fully connected layer to get the encoded visual feature . After that, we use another concatenation to combine and the textual representation . Thus, we obtain a query-conditioned feature encoding for the video, which can be denoted as , i.e., .
3.1.2 Video Summarization Prediction
Compact Video Encoding Module .
Given the generated query-conditioned feature representation from model , we introduce the compact video encoding module to learn the temporal dependencies among video shots. Thus the output of the compact video encoding can be produced as , where . The model consists of a Bidirectional LSTM (Bi-LSTM) layer [Graves and Schmidhuber(2005)]
to model the temporal representation, followed by a batch normalization layer[Ioffe and Szegedy(2015)]Glorot et al.(2011)Glorot, Bordes, and Bengio] to learn the compact video encoding.
Shot Score Prediction Module .
In order to predict a confidence score for each video shot, we propose the shot score prediction module . We define the confidence score as , where . In our setting, we use two fully connected layers with a batch normalization and a ReLU activation in the middle. After that, we apply a sigmoid layer in order to generate a confidence score for each video shot being a key shot.
Video Summary Generator .
Given the confidence score of each video shot from model , we introduce the video summary generator to generate the final results for selected key shots by means of scaling. To get the video summary results, we apply by passing the shot score into the video summary generator, where is the summary result for the shot, and . means that the shot is a trivial one, while
represents that it is a key shot which will be included in the generated summary.is a softmax function with a temperature parameter to get the result of each :
3.2 Discriminator Network
We use three pairs of different summaries together with the feature representation of the video as the input to the discriminator. For simplicity, we use , and to denote generated query-conditioned summary, ground-truth query-conditioned summary, and randomly generated query-conditioned summary, respectively. The three pairs are: (, video shots), (, video shots), and (, video shots). The video shots we use are the learn joint embedding of visual and query information. We use to enhance the ability of the generator to learn a more robust summary as well as avoid the generation of random trivial short sequences.
As shown in Figure 2, we use query-conditioned feature representation generated from model as the input of video shots in the three pairs as the learned feature for the video. is obtained using the random summary score and by generating random values of 0 and 1. The length of is the same as the one of predicted summary from the video summary generator . is produced using a ground-truth summary score , where . Note, , . In order to get , and , the three summary representations can be defined as:
After that, we take as the input to a Bi-LSTM layer, followed by a batch normalization layer and ReLU activation, and pass , and to another Bi-LSTM and a batch normalization layer with ReLU activation to learn a temporal representation. Then we concatenate them in pairs and apply three fully connected layers and jointly train the discriminator to distinguish the true summary from fake ones.
3.3 Adversarial Learning
We first introduce the summary regularization to optimize the generator by enforcing the selection of key shots to align with the ground-truth. It aligns a predicted shot score from the model with the corresponding ground-truth summary score :
We further incorporate the length regularization , which is computed between the number of generated summary shots and the ground-truth summary during the adversarial training to control the length of summaries:
where is the percentage of the key shots in the the video based on ground-truth summary, and is the summary result for each video shot generated from the model .
Our adversarial objective function is based on Wasserstein GANs [Arjovsky et al.(2017)Arjovsky, Chintala, and Bottou], due to its good convergence property. Note that this does not exclude the use of other GAN-based objectives, as our model is flexible enough to be combined with other GAN structures.
Instead of using the commonly used two-player learning mode, we optimize it with the three-player loss as shown in Figure 2: the real loss and the two fake losses and . The thee-player loss can not only force the model to generate good summaries, but can also avoid the learning of a trivial summary of randomly selected shots.
The generator and the discriminator conditioned on query are jointly optimized by the use of a min-max adversarial objective:
where is the balancing parameter for the two fake losses. Here we use for treating the two fake losses equally. We replace the generator with models , , in Section 3.1 and the three summary representation , and in Section 3.2, so that the objective function Eq. (5) can be reformulated as:
Thus, the final objective function conditioned on query including the two supervised losses, and , can be denoted as :
4 Experimental Results
4.1 Datasets and Settings
We evaluate our approach on the query-conditioned dataset proposed in [Sharghi et al.(2017)Sharghi, Laurel, and Gong], which is built upon the existing UT Egocentric (UTE) dataset [Lee et al.(2012)Lee, Ghosh, and Grauman]. The dataset has 4 videos in total, containing different uncontrolled daily lives scenarios and each being 35 hours long. A dictionary of concepts for user queries is supplied, which is a concise and diverse set of 48 concepts, which are deemed to be comprehensive of daily life for the query-conditioned video summarization. As for the queries, four different scenarios are included to formalize comprehensive queries [Sharghi et al.(2017)Sharghi, Laurel, and Gong]. Note that among different queries, one scenario is introduced where none of the concepts in the query are presented in the video. The three remaining scenarios are 1) queries, where all concepts appear together in the same video shot, 2) queries, where all concepts appear but not in the same shot, and 3) queries, where only one of the concepts appears. For fair comparison, we follow [Sharghi et al.(2017)Sharghi, Laurel, and Gong] and randomly select two videos for training, leaving one for testing and one for validation. Four experiments are performed to test all four videos.
In [Sharghi et al.(2017)Sharghi, Laurel, and Gong], the authors propose to find the ideal mapping between the generated query and the ground-truths summary by the maximum weight matching of a bipartite graph, based on a similarity function between two video shots. The similarity function uses the intersection-over-onion (IoU) on corresponding concepts to evaluate the performance. The IoU is defined as the edge weights, and the generated and ground-truths summaries are on opposing sides of the graph. Precision, recall, and F1-score are computed based on the number of matched summary pairs.
4.2 Implementation Details
We implement our work using TensorFlow[Abadi et al.(2015)Abadi, Agarwal, Barham, Brevdo, Chen, Citro, Corrado, Davis, Dean, Devin, et al.], with 1 GTX TITAN X 12GB card on a single server. In the generator , the output for frame- and shot-level visual representation are 2048- and 4096-dimensional vectors, and the textual representation is a 300-dimensional vector. The learned temporal representation after the Bi-LSTM is 2048-dimension. In the module , the output of the two fully connected layers are 128- and 1-dimensional vectors, respectively. We use a low-temperature softmax function in module in order to get approximately binary results. The dropout between the two fully connected layers is 0.5.
In the discriminator , the Bi-LSTM that we use encodes the features to a 512-dimensional vector. The output dimensions for the three fully connected layers are 512-, 256-, and 128-dimensions. During the training phase, we randomly select a set of 1000 successive video shots for each batch, with one user query for all shots. For the generic scenario where none of the concepts in the query are presented in the video, we use a zero vector of 300-dimension for the query embedding. During the testing phase, we obtain the predicted shot score in module for each video shot.
The inference times for the 4 videos are 1472, 1948, 1141 and 1893, respectively, so on average, it takes 1.614 for each video to generate query-conditioned key video shots.
4.3 Quantitative Results
4.3.1 Comparison Analysis
We compare our approach with all other frameworks which have been applied to this query-conditioned video summarization dataset. The precision, recall and F1-score comparison for the four videos are shown in Table 1. It can be observed that our approach outperforms the existing state-of-the-art by 1.86%. Especially for Video 2 and Video 4, we achieve 3.64% and 3.68% better performance than [Sharghi et al.(2017)Sharghi, Laurel, and Gong] in terms of F1-score. Such substantial performance improvements indicate the superiority of our proposed method by using a three-player adversarial network on the joint embedding of visual information and the user query. The rest three works are all based on a DPP architecture, which can learn long time temporal relations among video shots. However, our work adopts the adversarial learning objective, which facilitates both temporal and query-conditioned joint learning. The two regularizations on summary and length also help obtaining better query-conditioned summary.
|SeqDPP [Gong et al.(2014)Gong, Chao, Grauman, and Sha]||SH-DPP [Sharghi et al.(2016)Sharghi, Gong, and Shah]||QC-DPP [Sharghi et al.(2017)Sharghi, Laurel, and Gong]||Ours|
4.3.2 Ablation Analysis
We conduct experiments on different components of our model. As shown in Table 2, we use , and to denote our model when trained without the length regularization loss, the ground-truth summary regularization loss, and the random summary loss respectively. We can observe that the performance is reduced slightly after dropping the length regularization and the random summary as a form of two-player structure. Thus it demonstrates the effects of the length regularization and the three-player manner. Besides, there is a large decline after dropping the ground-truth summary regularization, which complies with the fact that additional supervised information tends to improve learning considerably.
We further conduct an experiment to more thoroughly analyze the ability of our proposed summary length regularization approach to generate summaries of suitable length. Here we define the summary length distance between the generated summary and the ground-truth summary as: , is the total number of queries. We use and to denote the summary result and the ground-truth summary given a certain query with the length regularization . Similarly, the summary distance between the generated summary after dropping the length regularization and the ground-truth summary is defined as: , denotes the summary result given a certain query after dropping the length regularization .
As shown in Table 3
, the F1-score of the model without length regularization is reduced by 1.93% in average, compared with the result of our proposed framework, and the length distance increases from 17.32 to 40.88. This demonstrates the effect of the length regularization. Moreover, we can also observe that the differences between precision and recall values fortend to be larger than the ones in our proposed approach. Note that the smaller the distance between precision and recall values is, the closer between the length of the summary and the length of the ground-truth is. This indicates the effects of our introduced query length regularization .
4.4 Qualitative Results
We provide some visualization results of our method in Figure. 3. We use the two user queries as examples: “Book Tree” and “Book Lady” (each user query contains two concepts). The x-axis in the figure is the shot number given a certain video. The upper blue lines denote the ground-truth key shots which are related to the user query, while the bottom green lines represent predicted key shots using our proposed method. Note that the selected summaries can be either related to one or two concepts given a user query. We can observe that our proposed method can find compact and representative summaries.
In this paper, we proposed a query-conditioned three-player generative adversarial network for query-conditioned video summarization. In the generator, video representations conditioned on user queries are obtained by jointly encoding visual information together with the text of user queries. Given these embeddings, confidence scores are predicted for each video shot in order to generate key shots based on these predicted scores. In the discriminator, we defined a three-player loss by introducing a randomly generated summary to prevent the model from generating trivial and short sequences. Experiments on videos of uncontrolled daily lives demonstrate the superiority of our proposed method.
This project is supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. This work is also partially funded by the National Natural Science Foundation of China (Grant No. 61673378 and 61333016), and Norwegian Research Council FRIPRO grant no. 239844 on developing the Next Generation Learning Machines.
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