The popularity of wearable cameras in recent years is accompanied by a large number of first-person view videos, or often called egocentric videos, that record persons’ daily interactions with their surrounding environments. The demand for automatic analysis of egocentric videos has promoted various egocentric vision techniques  such as egocentric video hyper-lapse [20, 35] and video summarization [26, 48]. In particular, the task of understanding what a person is doing and where a person is looking at have attracted great interests from researchers. The former task is often called egocentric action recognition [24, 27, 52] and the latter is called egocentric gaze prediction [22, 55, 13]. Although the two tasks have been studied extensively in the past years, few works have focused on the relationships between the two tasks which are in fact deeply related.
This work aims to jointly model the two coupled tasks of gaze prediction and action recognition in egocentric videos. Previous works have studied how gaze could be used for action recognition [7, 23]. They tried to model human gaze in egocentric videos and use estimated gaze points for removing unrelated background information, in order to improve action recognition. While the guidance of gaze for action recognition has been studied, gaze itself was simply modeled as a saliency prediction problem, and no effort has been seen to explicitly explore the influence of actions for gaze prediction.
In an egocentric video, background regions are often cluttered and may contain multiple salient regions. Thus it would be difficult for a saliency-based model to predict gaze reliably without additional information about the location of attention. It has been studied by psychologists that an action performed by a person implicitly affects where the person is looking [45, 46, 38]. For example, to take a knife from a table, a person always moves his/her focus onto the knife and then keeps fixating on the knife before grasping it. Also, when people performing same daily actions like “put cup” or “take plate” they often have similar gaze movements. Therefore, we argue that for better modeling of gaze and actions in egocentric videos, not only the gaze-guided action recognition (gaze context for actions) but also the action-dependent gaze prediction (action context for gaze) should be jointly considered.
In this paper, we propose a mutual context network (MCN) that jointly predicts human gaze and recognizes actions in egocentric videos with consideration of mutual context between the two coupled tasks. The proposed MCN takes a video sequence as input and outputs action likelihood as well as a gaze probability map for each frame. Two novel modules are developed within the model to leverage the context from the predicted actions and gaze probability maps respectively. The first module called the action-based gaze prediction module takes the predicted action likelihood as input and produces a set of convolutional kernels that are relevant to the action being performed. The generated action kernels are then used to convolve input feature maps for locating action-related regions. The second module called the gaze-guided action recognition module uses the estimated gaze point as a guideline to spatially aggregate the input features for action recognition. Rather than only using the region around the gaze point as in previous work, the features are aggregated both in the gaze region and the non-gaze region separately and then used as input to the gaze-guided action recognition module, while the relative importance of the two regions is learned automatically during training.
Our main contributions are summarized as follows:
We propose a novel MCN for both egocentric gaze prediction and action recognition that leverages the mutual context between the two tasks.
We propose a novel action-based gaze prediction module that explicitly utilizes information from the estimated action for gaze prediction. This is done by generating convolution kernels for gaze prediction adaptively with the estimated action.
Our proposed MCN achieves state-of-the-art performance in both gaze prediction and action recognition and is able to learn action-dependent gaze patterns.
2 Related works
2.1 Egocentric gaze prediction
Predicting gaze in an egocentric video can benefit a diverse range of applications such as joint attention discovery [12, 16, 30], action recognition , human computer interaction [8, 17, 21], and video summarization . Despite the correlation between gaze and saliency , previous works have revealed the need for additional cues for predicting gaze in egocentric videos [22, 44, 50, 51, 55, 56]. Li  used head motion and hand cues in a graphical model for gaze prediction. However, the pre-defined egocentric cues may limit the generalization ability of their model. Huang  proposed a hybrid deep model which incorporates task-dependent gaze shift patterns in addition to a bottom-up saliency-based model. However, they did not consider the differences in gaze patterns with respect to different actions.
In this work, we explicitly leverage the contextual information from the performed actions for gaze prediction by using the predicted action likelihood. To the best of our knowledge, this is the first work to explore the influence of actions for egocentric gaze prediction.
2.2 Egocentric action recognition
Egocentric action recognition is one of the focused fields in egocentric vision and has been studied extensively in recent years [9, 24, 25, 28, 29, 34, 33, 41, 43, 53, 54, 4]. Kitani  used global motion to discover different egocentric actions in an unsupervised manner. Fathi  adopted a graphical model to recognize actions in relation to objects and head/hand motion. Ryoo  proposed a novel pooling method for action recognition. Ma  proposed a comprehensive deep model for recognizing objects and actions jointly. Singh  used additional inputs like hand masks to improve action recognition performance. Sudhakaran 
used object-centric attention in a recurrent neural network to get better performance in action recognition. Different from previous works, our method recognizes actions with the contextual information from gaze by modeling actions and gaze in a unified framework.
2.3 Gaze and actions
Human gaze and actions are deeply correlated in egocentric videos, and the use of gaze has been proved to be beneficial for action recognition [7, 39, 57]. However, little work has been done on the joint modeling of egocentric gaze prediction and action recognition. Extended from , Li  proposed a deep model for jointly modeling gaze and actions. They modeled the probabilistic nature of gaze and used the estimated gaze for better action recognition. However, their work did not explicitly consider the contextual information from actions for gaze prediction. Gaze prediction will be less reliable without the contextual information of actions.
In this work, we leverage the mutual context of gaze and actions in our proposed model, in the form of using action likelihood as a conditional input to predict gaze and simultaneously, using gaze as a guidance for action recognition. By explicitly exploring such mutual context, our model achieves state of the art performance in both gaze prediction and action recognition.
3 Our proposed MCN
When performing a task, especially a hand manipulation task, human gaze and actions of hand-object interaction are tightly related. While image region around a person’s gaze point explicitly reveals important information about the undergoing action, the action performed by the person implicitly affects where the person is looking. In this work, we propose a mutual context network (MCN) that uses the estimated action to predict gaze point and uses gaze as a guidance for action recognition.
Figure 2 depicts the architecture of our MCN. The input video RGB frames and optical flow images are first encoded as feature maps by the feature encoding module, which are then used as input to the following modules. One of the key components in our model is the action-based gaze prediction module that learns to predict gaze using the predicted action likelihood as a conditional input. As complementary information for gaze prediction, we also obtain a saliency map with the saliency-based gaze prediction module. The outputs from the two modules are then fused by the late fusion module to get the final gaze probability map . Another component in our MCN is the gaze-guided action recognition module which takes the predicted gaze as guidance to selectively filter the input features for action recognition. The output of action likelihood is then used as conditional input to the action-based gaze prediction module, thus a loop of mutual context is constructed.
3.2 Feature encoding module
We adopt the first four convolutional blocks of the 3D convolution network I3D  for feature encoding. Following , we fuse the RGB stream and optical flow stream at the end of the 4th convolutional block by element-wise summation. With this 3D encoder, the output feature map is of size , where is the number of channels, is the temporal dimension, and are the spatial height and width.
3.3 Saliency-based gaze prediction module
Image regions with high saliency tend to attract human attention. For instance, regions with unique and distinguishing features such as a moving object or high contrast of brightness are more likely to be looked at than other regions. Therefore, we use a saliency-based gaze prediction module to learn the image regions that are more likely to draw human attention. For this, we use a 3D decoder that takes the encoded feature map as input and outputs a series of gaze probability maps with each pixel value within the range of [0, 1]. While this bottom-up approach provides information about salient regions in the image, it is not sufficient to reliably identify the attended region when multiple salient regions exist, which is common in egocentric video.
3.4 Action-based gaze prediction module
As different actions are associated with different objects and motion, the gaze patterns when performing different actions are different. It is necessary for the gaze prediction module to leverage action information for more reliable gaze prediction. To this end, inspired by [49, 5], we use the output of the gaze guided action recognition module to generate a group of convolutional kernels that are used to identify the regions relevant to the performed action. The generated action kernels are then used to convolve with the input features in order to locate the action-related regions. Finally, gaze probability maps that have the same size with input frames are generated by a decoder consisting of deconvolutional layers.
More formally, given action likelihood estimated by the action recognition module and the input feature maps with channels ( and are temporal and spatial dimension), the gaze probability map is generated through the following procedure:
where is the kernel generator, is a group of kernels, and is the filtered feature maps. denotes the operator of convolution. The kernel generator contains one fully connected layer and two convolutional layers. The output of the first fully connected layer is first reshaped into size and then forwarded to the following convolution layers.
We also adopt the saliency-based gaze prediction module which can be seen as a complementary to the action-based gaze prediction module. Finally, we use a late fusion module to combine the outputs and from the previous modules:
Late fusion technique has been proved to be effective in previous work of gaze prediction . Following previous works [55, 22], we take the spatial location with maximum likelihood on as the predicted gaze point.
3.5 Gaze-guided action recognition module
Here we describe the gaze-guided action recognition module in our MCN that uses the predicted gaze point as a guide to exploit discriminative features for action recognition. Previous works [7, 23] mostly used gaze as a filter to remove features of image regions far from the gaze point. However, focusing only on the region around the gaze point might lose important information about the action. We observed that when performing certain actions such as “put an object”, the person may fixate on the table on which to place the object instead of looking at the object in hand which contains critical information about the action. Therefore, we think that while the gaze region is important, the region outside the gaze (non-gaze region) might also contain complementary information about the action. In this work, we develop a two-way pooling structure to aggregate features in the gaze and non-gaze regions separately and use both as input for action recognition.
As shown in Figure 2, we first forward to the fifth convolutional block of I3D to encode more compact features . On each temporal dimension of , we locate the corresponding spatial gaze point
on the feature map by selecting the maximum spatial location of the 3d max-pooled gaze map. Then we split spatial dimensions of the feature map into two parts: gaze region and non-gaze region. Gaze region on a feature map (dark green region of in the figure) is the locations whose spatial positions are within range , and non-gaze region is the left-out region (light green region of
in the figure). We pool the two regions separately on the spatial dimensions, generating two feature tensorsand :
where denotes the -th channel and position of the feature map , similarly for .
The pooled feature tensors and are fed into two 1x1x1 convolution layers (denoted as ), and the outputs are channel-wise concatenated and forwarded into the final 1x1x1 convolution layer (denoted as ) for predictions. We average the predictions on temporal dimension to get the action likelihood :
Here denotes channel-wise concatenation. We set the output channel of to be and to be since the modeling of non-gaze region is empirically simpler than that of the gaze region, so we limit its channel size to prevent over-fitting.
3.6 Implementation and training details
The whole framework is implemented using Pytorch framework. The feature encoding module is identical to the first 4 convolutional blocks of the I3D  network without the last pooling layer. With our input of 24 stacked images of size , the output of feature encoding module is of size . The decoder contains a set of 4 transposed convolution layers, with kernel sizes
, and stride
respectively. Padding 1 is added on all layers. Each layer is followed by batch normalization and ReLU activation. We add another convolution layer with kernel size 1 and a sigmoid layer on top of the decoder for outputting values within
. The kernel decoder takes the input vectorwhere is the number of action categories, and firstly encoded to a latent size of and reshaped into . The two convolutional layers output channels and , with kernel size 3, stride 1 and padding 1. The output size of the action kernel generator is . For the gaze guided action recognition module, the convolution block is identical to the -th convolution block of the I3D network. Thus the output size of is . The 3d max-pooling layer therefore has kernel size (8,32,32). We set and . The late fusion module is composed of 4 convolutional layers with output channels 32,32,8,1, in which the first 3 layers have a kernel size of 3 with 1 zero padding and the last layer has a kernel size of 1 with no padding.
For training the whole network, we first train the gaze-guided action recognition module and the saliency-based gaze prediction module using ground truth action labels and gaze positions. We use Adam optimizer  in all experiments. The base I3D weights are initialized from weights pretrained on kinetics dataset . We then use the result of action recognition to train the action-based gaze prediction module and then the late fusion module. We use cross entropy loss for action recognition and binary cross entropy loss for gaze prediction. We apply a Gaussian with on the gaze point for generating ground truth images for gaze prediction. The learning rates for action recognition module and all gaze prediction modules are fixed as and respectively. We first resize the images to and then random crop images into , random flip with probability 0.5 for data augmentation during training. Ground truth gaze images perform the same data augmentation. When testing, we resize the image and send both the images and their flipped version and report the averaged performance.
We iteratively infer gaze positions and action likelihood vectors in an alternative fashion as described in Algorithm 1. The iteration terminates when the variation (measured by average angular error AAE) of current gaze prediction from the previous prediction is below a threshold or the number of iteration surpasses an upper bound.
4.1 Dataset and evaluation metric
Our experiments are conducted on two public datasets: EGTEA  and GTEA Gaze+ . The GTEA Gaze+ dataset consists of 7 activities performed by 5 subjects. Each video clip is 10 to 15 minutes with resolution . We do 5-fold cross validation across all 5 subjects and take their average for evaluation as . The EGTEA dataset is an extension of GTEA Gaze+ which contains 29 hours of egocentric videos with the resolution of and 24 fps, taken from 86 unique sessions with 32 subjects performing meal preparation tasks in a kitchen environment. Fine-grained annotations of 106 action classes are provided together with measured ground truth gaze points on all frames. Following , we use the first split (8299 training and 2022 testing instances) of the dataset to evaluate the performance of gaze prediction and action recognition. We use the trimmed action clips of both datasets for training and testing unless otherwise noted.
We compare different methods on both tasks of gaze prediction and action recognition. For gaze prediction, we adopt two commonly used evaluation metrics: AAE (Average Angular Error in degrees) and AUC (Area Under Curve) . For action recognition, we use classification accuracy as the evaluation metric.
4.2 Gaze prediction results
We compare our method with the following baselines:
Egocentric gaze prediction methods: We also compare with three egocentric gaze prediction methods most related to our work: coarse gaze prediction method (Li ), the GAN-based method (DFG ), and the attention transition-based method (Huang ). Since  only outputs a coarse gaze prediction map (of resolution
), we resize their output using bilinear interpolation. ForLi and DFG we report the results based on our implementation as no code is publicly available. For Huang we use the author’s original implementation. However, Huang is designed for untrimmed video since it needs knowledge about continuous attention transition. Therefore, we also report the performance of Huang † trained using the full untrimmed dataset.
Subsets of our full MCN: We also conduct ablation study using subsets of our full model. These include the saliency-based gaze prediction module (Saliency-based), the action-based gaze prediction module (Action-based). In addition, we also test the action-based gaze prediction module with ground truth action labels (Action-based).
|Our full MCN||5.79||0.932||5.74||0.945|
Table 1 shows the quantitative comparison of different methods on gaze prediction performance. Although our saliency-based gaze prediction module alone cannot get preferable performances against state-of-the-art gaze prediction methods of  and , our action-based gaze prediction module clearly outperforms all previous methods trained on the same dataset. This demonstrates the usefulness of actions in gaze prediction. Our full MCN further outperforms the action-based saliency prediction module, indicating that an ideal gaze prediction method should consider information from both bottom-up visual saliency and top-down influence of actions. The superiority of encoder-decoder based SALICON  over  reveals the importance of using decoder-based structure for fine-grained gaze prediction.
Importantly to be noted that Huang † trained with untrimmed videos outperforms our method by the metric of AAE on GTEA Gaze+ dataset. The comparison between our method and the two variants of Huang and Huang † shows that while our method can benefit from action-based gaze prediction and achieves state-of-the-art performance on the trimmed dataset, its current version could not fully explore the useful information from additional data in the untrimmed dataset. This indicates a potential research direction and would be discussed as our future work in Section 5.
Comparing subsets of our MCN, the action-based module performs better than the saliency-based module and even better than the state-of-the-art methods, indicating the effectiveness of action information in gaze prediction. When feeding the action-based module with ground-truth action labels, the performance is further improved. The performance of our full MCN significantly improves by integrating the two sub-modules. This strongly indicates that the action-based gaze prediction module and the saliency-based gaze prediction module represent complementary information and should be jointly considered.
Qualitative results are shown in Figure 3. It can be seen that with the help of the action-based gaze prediction module, our full MCN can better locate the action, thus giving better gaze prediction results. For example, in the first row, our MCN successfully recognizes the action as “take paper towel”, thus finds the paper towel in the hand. Other baseline methods mostly focus on the stove or other salient regions. In the second row, while other methods are distracted by the plates and food on the counter, our MCN successfully locates the hand with dishrag on the bottom right corner and a part of the counter which will be cleaned in the next few frames. More interestingly as shown in the fourth row, the lettuce of ground-truth gaze fixation is placed on a cluttered kitchen table, which is challenging for other methods to locate. Still, our full MCN correctly predicts gaze to be on the lettuce with the help of context from the action “take lettuce”. Similar situations can be found in other rows of the figure.
4.3 Examination of action-based gaze prediction module
We conduct a new experiment on the top 20 frequent actions in the test set of EGTEA dataset to examine our action-based gaze prediction module. We feed the module with action label representing each of the 20 action classes and examine how gaze prediction performance (AAE score) varies when the module is tested on each of these actions. For example, we feed the action-based gaze prediction module with the action label of “take plate” and test the AAE scores on the videos of all 20 actions. As a result, we obtain a matrix of AAE scores with the size of , denoted by , in which is the AAE score of the action-based gaze module fed with the action label of the -th action and applied to the videos of the -th action.
We found that the average AAE score on the diagonal of is 6.21, while the average AAE score of on non-diagonal locations is 6.87. This indicates that the action-based gaze prediction module benefits more from correct action information. We also observe that there exists several action groups (e.g. “cut something”) that feeding the module with incorrect action labels within the same group does not affect gaze prediction performance too much. Please see the supplementary material for more detailed explanation of experimental setting and result analysis.
4.4 Action recognition results
As for the task of action recognition, we compare our method with the following baselines:
Methods using measured gaze: I3D+Gaze is to use a ground truth gaze point as a guideline to pool feature maps from the last convolution layer of the fifth convolutional block. EgoIDT+Gaze  is a traditional method which uses dense trajectories  selected by a ground truth gaze point for action recognition.
State-of-the-art egocentric action recognition methods: Li  uses a estimated gaze probability map as soft attention to perform a weighted average on top I3D features. Sudhakaran  adopts attention mechanism in a recurrent neural network to recognize actions. We also compare our method with Ma  and Shen  that use additional annotations of object locations and hand masks during training.  even uses ground-truth gaze positions as input during testing. We compare the performance as reported in their original papers.
Baseline of our model: MCN (gaze region) is a baseline of our MCN that uses only the gaze region for pooling without using non-gaze regions. We use this baseline to validate the usefulness of information from the non-gaze regions.
|EgoIDT + Gaze ||46.50||60.50|
|I3D  + Gaze||51.21||59.72|
|MCN (gaze region)||52.35||59.21|
|Our full MCN||55.63||61.14|
lists the action recognition accuracy of our model and baseline methods. The deep learning method I3D outperforms EgoIDT+Gaze  that uses handcrafted features on EGTEA dataset but not on GTEA Gaze+ dataset. This is possibly due to the smaller number of training samples in GTEA Gaze+ dataset. With the use of measured gaze, the performance of I3D+Gaze is slightly improved compared with I3D. On both datasets, our MCN performs the best among all the methods except for  and  that rely on additional labeling. We also conduct ablation study and compare with our baseline that only uses gaze region for action recognition. The superiority of our MCN over the baseline validates our thought that the non-gaze regions contain supplementary information and should be jointly considered in action recognition.
Also, we show the performance of our method during the alternative inference procedure in Figure 4. It can be seen that the performance of both gaze prediction and action recognition increases in the first two iterations and saturates from then. Our method outperforms the strongest baselines on both tasks after the first iteration. This strongly supports our hypothesis that the mutual context of gaze and action can be beneficial for both tasks.
4.5 Failure cases and discussion
Here we discuss several failure cases we faced. One failure case comes from the inaccuracy of action recognition. As shown in the first row of Figure 5, although we use a late fusion module to fuse the outputs of the sub-modules, our model may fail when action recognition gives a wrong result. Still, the impact of failed action recognition is limited in our model. Our MCN can still outperform other methods: among all the testing data of the EGTEA dataset, our model achieves an AAE score of when action recognition fails, and an AAE score of when action recognition is correct.
Another failure case comes from the circumstances where a person begins to shift the gaze fixation between consecutive actions. An example is shown in the second row of Figure 5. After grabbing the bread, instead of keeping fixation on the bread, the person’s attention goes to the plate on which he’s planning to put the bread. Other than increasing the action recognition accuracy, this reveals the necessity of taking attention transition  into consideration for our current gaze prediction model.
5 Conclusion and future work
In this work, we proposed a novel deep model for both egocentric gaze prediction and action recognition. Our model explicitly leverages the mutual context between the two tasks and achieves state of the art performance in both tasks on a public egocentric video dataset. Although our model can reliably predict gaze within an action period compared with previous methods, gaze prediction performance still needs further improvement, especially for the transition moment between consecutive actions. We think it would be an interesting future work to explore the gaze transition patterns relevant to the performed actions.
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