Video captioning is an active research topic in the multimedia field. Most studies on this topic have attempted to describe events using third-person video data (Chen et al., 2019), but these studies have demonstrated the difficulty in describing finer-grained activities such as cooking because these activities require more detailed information than third-person video data. Egocentric video, which is recorded by using a wearable camera, is useful for this purpose because it can produce much finer-grained information on the activities of the camera wearer on the basis of the closer view (Figures 1 (a)–(b)). However, video captioning based on the egocentric video is not trivial because it, in nature, often suffers from motion blur, self-occlusion, and out-of-camera-range activities (Figures 1 (c)–(d)). It is therefore better to utilize supplementary information to overcome these difficulties.
This paper proposes a new task of sensor-augmented egocentric-video captioning for the finer-grained description of human activities. It utilizes egocentric-video data along with motion data from wearable sensors, or inertial measurement unit (IMU) sensors. For this task, we constructed a new dataset called MMAC Captions111The dataset will be released to facilitate further research. by extending the CMU-Multimodal Activity (CMU-MMAC) dataset (Spriggs et al., 2009). MMAC Captions contains 16 hours of egocentric-video and wearable-sensor data with more than 5,000 egocentric activity descriptions.
One of the difficulties of the proposed task is how to fuse the differing video and sensor modalities. Although both of these complementary modalities are useful in many situations, one modality could have a negative impact on performance. For example, sensor data may occasionally contain undesirable noise caused by surrounding objects or by irrelevant motions to the target activity (Figures 1 (e)–(f)). In such cases, not using sensor data or assigning less weights to it is preferable. To this end, we propose a dynamic modal attention (DMA) module, which determines the modalities to be emphasized more (or less) depending on the context.
We compared the proposed sensor-fusion method with strong baselines on the MMAC Captions dataset. The experimental results showed that the sensor data was beneficial as supplementary information to the egocentric video data, and that our DMA module effectively determined the modalities to be emphasized.
The key contributions of this study are summarized as follows. (1) We propose a new task of sensor-augmented egocentric-video captioning for the finer-grained description of human activities, and provide a newly constructed MMAC Captions dataset that contains 16 hours of egocentric video and sensor data with more than 5,000 egocentric activity descriptions. (2) We propose a DMA module that determines the modalities to be emphasized more (or less) depending on the contexts. (3) We experimentally demonstrate the effectiveness of using sensor data and that of the DMA module through comparisons with strong baseline methods.
2. Related Work
Image and Video Captioning
There has been much research on generating textual descriptions from images and videos (Chen et al., 2019). One approach is phrase-based captioning, in which an image is first converted into several phrases using object recognition and action recognition, then connecting them with textual modeling (Ushiku et al., 2011; Ordonez et al., 2011; Kulkarni et al., 2013; Toderici et al., 2010; Guadarrama et al., 2013; Rohrbach et al., 2013; Kuznetsova et al., 2012; Ushiku et al., 2015; Lebret et al., 2015). This approach can avoid critical mistakes because it uses a prefixed word sequence; however, it is difficult to generate natural sentences. Another approach is to use an encoder-decoder model for image and video captioning (Karpathy and Toderici, 2014; Vinyals et al., 2015; Chen and Zitnick, 2015; Mao et al., 2015; Liu et al., 2016; Xu et al., 2015; Wu et al., 2016; Venugopalan et al., 2015a; Donahue et al., 2015; Venugopalan et al., 2016; Yao et al., 2015; Baraldi et al., 2017; Pan et al., 2016; Hendricks et al., 2016; Krishna et al., 2017). The pioneering work by Venugopalan et al. (Venugopalan et al., 2015a)
uses a sequence-to-sequence model that combines a convolutional neural network (CNN)-based image encoder and long short-term memory (LSTM)-based textual decoder. Many studies have proposed extensions to this work from various perspectives to improve performance; selective encoding(Chen et al., 2018c), boundary-aware encoding (Baraldi et al., 2017), adaptive attention (Lu et al., 2017), word-transition molding (Chen et al., 2018b; Ke et al., 2019), and object relational modeling (Zhang et al., 2020; Aafaq et al., 2019; Zhou et al., 2019; Pan et al., 2020). Transformer (Polosukhin, 2017) has been proven to be more effective for video captioning (Chen et al., 2018a; Zhou et al., 2018; Sun et al., 2019; Pan et al., 2020; Lei et al., 2020; Ging et al., 2020; Jin et al., 2020). Zhou et al. (Zhou et al., 2018)
first proposed the end-to-end transformer model for dense video captioning. Leiet al. (Lei et al., 2020) introduced a memory-augmented recurrent transformer based on bidirectional encoder representations from transformers (BERT) (Devlin et al., 2019).
Several works have explored captioning based on multi-modal information such as that from video and audio information (Ramanishka et al., 2016), and multivariate well logs (Tong et al., 2017). One of the typical methods for combining multi-modal information is to use multi-modal attention (Rahman et al., 2019; Hori et al., 2017; Xu et al., 2017). The attention mechanism has been further explored with the transformer architecture for multi-modal video captioning. Iashin et al. proposed a feature transformer for video and audio input (Iashin and Rahtu, 2020b) and bi-modal attention for both modalities (Iashin and Rahtu, 2020a).
Our work expands upon these prior works and exploits the attention-based modality-integration module for dynamically changing emphasis on different video and sensor modalities depending on the contexts.
Activity Recognition using Wearable Camera and Sensors
There is a substantial body of work on egocentric vision, ranging from activity recognition to video summarization (Nguyen et al., 2016; Betancourt et al., 2015). One of the following cues is commonly used in activity recognition; a motion cue, which uses coherent motion patterns for egocentric video (Kitani et al., 2011; Poleg et al., 2015; Ryoo et al., 2015; Yonetani and Kitani, 2016; Singh et al., 2017), an object cue, which uses objects in video sequences (Wang et al., 2020; Hamed and Ramanan, 2012; Fathi and Rehg, 2013; Fathi et al., 2011a; Lee et al., 2012; Fathi et al., 2011b; Damen and Calway, 2014), or an integrated cue, which uses both motion and object information (Li et al., 2013; Fathi et al., 2012; Ohnishi et al., 2015; Ma et al., 2016; Li et al., 2015; Al-Naser et al., 2018). Much effort has been put into constructing applications (Nagarajan et al., 2020; Ng et al., 2020; Yuan and Kitani, 2019; Fan and Crandall, 2016; Bolaños et al., 2018; Ohnishi et al., 2015) and datasets (Damen et al., 2018; Spriggs et al., 2009; Nakamura et al., 2017; Kong et al., 2019; Shan et al., 2020), too.
Several studies have used non-visual wearable sensors for activity recognition, including accelerometers and heart rate sensors (Nakanishi et al., 2015; Nakamura et al., 2017; Ohashi et al., 2017, 2018). Bao et al. (Bao and Intille, 2004)
used multiple accelerometers on the hip, wrist, arm, ankle, and thigh to classify 20 classes of household activities. Spriggset al. (Spriggs et al., 2009) used an egocentric camera, IMUs, and other sensors to classify 29 classes of kitchen activities. Maekawa et al. (Maekawa et al., 2010) used a wrist-mounted camera and sensors to detect activities in daily life. The present study extends these prior works to tackle a newly proposed task of sensor-augmented egocentric-video captioning.
3. MMAC Captions Dataset
The CMU-MMAC dataset (Spriggs et al., 2009) is the first dataset that augments egocentric video with wearable IMU data. The sampling rate is 30 fps with a resolution of both 800600 and 1024768 for egocentric video and a maximum of 125 Hz for 9-axes IMUs (a 3-axes gyroscope, 3-axes accelerometer, and 3-axes compass). 9 IMUs were attached to the subject’s body: both forearms/upper arms, both thighs/lower limbs, and back. A Point Grey FL2-08S2C-C camera was used for capturing egocentric video. To construct this dataset, 43 participants wore a wearable camera and IMU sensors and performed five types of cooking activities in a kitchen (i.e., Brownie: making brownies, Salad: preparing a salad, Pizza: making pizza, Eggs: frying eggs, and Sandwich: making a sandwich). Although several types of annotations including object and action classes are provided222http://kitchen.cs.cmu.edu/., detailed activity descriptions are not.
We therefore introduce a new dataset called MMAC Captions, which contains 5,002 activity descriptions for 16 hours of egocentric data. Two annotators were independently asked to first define a “segment”, which corresponds to a short period that contains only one activity, by determining the timestamps of the start and end of the segment. Second, they were asked to provide one sentence per segment. Finally, cross checking was carried out to integrate multiple captions into one consistent sentence after each annotator finished their own annotations. We omit the subject of the sentences in the annotations because the subject is always the person who wears the sensors and camera. The dataset will be made publicly available.
A number of activity description examples are shown as follows.
- Spreading tomato sauce on pizza crust with a spoon.
- Taking a fork, knife, and peeler from a drawer.
- Cutting a zucchini in half with a kitchen knife.
- Moving a paper plate slightly to the left.
- Stirring brownie batter with a fork.
We found that the provided annotations were much more diverse than those of the action or object categories in a sense that there exist multiple objectives, adverbs, and a wide distribution of sentence lengths.
We used the Natural Language Toolkit (NLTK)333https://www.nltk.org/ and TreeTagger444https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ to analyze the dataset, resulting in observing 131 noun classes and 89 verb classes as shown in Figures 2 (a) and (b). Figure 2 (c) shows the distribution of word-types at each word step. All the sentences start with a verb, and a determiner and noun pair follows in many cases. The average sentence length is seven words, and the maximum length is 14 words. The average duration of a segment is 6.7 seconds, and the median is 3.0 seconds. Figure 2 (d) shows that sensor data from an IMU, such as the gyroscope, are useful to predict activities. For example, shaking and turning
take a significantly higher variance than other activities, suggesting that IMU signals are effective for activity description.
The flow of our approach is shown in Figure 3. Our goal is to describe activities using egocentric-video and wearable-sensor data. To achieve this goal, we extend the boundary-aware neural encoding model (Baraldi et al., 2017) for multi-modal activity descriptions.
The core idea includes two key aspects. First, we model a sensor encoder with recurrent neural networks and introduce a learnable transformation module to effectively integrate multi-modal features. Second, we propose a step-wise modality-attention module to dynamically determine the best fusion of different representations for generating each word in a sentence.
4.1. Multi-modal Encoder
The input to our model is a sequence of video frames and wearable-sensor signals , where is a video frame at time , and
is a 63-dimensional vector at timefrom 9-axis IMUs attached at seven locations on the body555There were originally nine locations, but the data collected from two sensors were incomplete, and were therefore not included in the CMU-MMAC dataset..
We developed a visual encoder based on the boundary-aware neural encoder (Baraldi et al., 2017) that can identify discontinuity points between frames such as appearance change and motion change. Our intuition is that such discontinuity frequently occurs in egocentric videos because there are many undesirable imaging situations such as motion blur, frame out, illuminance change, and self-occlusion. Therefore, we assumed a boundary detector would work efficiently. Given a video sequence, we compute a visual representation as , where represents the final hidden state of the LSTM layer.
Unlike in , we do not use the boundary detector in sensor encoder
. In many cases, boundary detection occurs too often during the sensor-encoding step, which results in an enormous state initialization. We therefore developed a neural encoder based on the LSTM layers without boundary-aware encoding. Raw sensor signals are first resampled at 30 Hz using a piecewise linear interpolation and directly fed into the LSTM input layer. With the sensor signals, we compute sensor representation as, where is also the final hidden state of the LSTM layer.
Asymmetric Multi-modal Transformation (AMMT)
Once the representations of video and sensor have been encoded, the decoder can generate a textual description by simply concatenating both representations (i.e., .
However, the simple concatenation may not be optimal because the amount of useful information contained in each representation vector may be significantly different due to the difference in their dimensions, or the length of the representation vectors. To mitigate the possible imbalance, we introduce a learnable transformation module called the asymmetric multi-modal transformation (AMMT), which is inspired by feature-wise linear modulation (Perez et al., 2018). AMMT is defined as follows:
is the weight matrix representing a linear transformation andis a bias. The and are learnable vectors, which are initially set to identity transformation (i.e., and ). In other words, we use a standard concatenation in the initial phase and update parameters during the training phase.
The intuition behind the design of asymmetry, or applying linear transformation only to , is two-fold. First, the mitigation of the aforementioned possible imbalance can be achieved by applying the transformation to one of the representations rather than both; the latter has a risk of over-fitting due to the redundant parameters. Second, because the sensor data sometimes contain undesirable noise, which is explained in Section 1, adjusting the sensor representation so that it does not adversely affect the performance is preferable. In addition to this integrated representation , AMMT forwards and as well because there are a number of cases where using only single modality is preferable (e.g., sensor data containing undesirable noise).
4.2. Multi-modal Decoder
Given the aforementioned representations from the multi-modal encoder, our decoder generates a sentence , where is a word with one-hot-vector encoding at step . The objective function for sentence generation is
where denotes all parameters in the model, and
is a set of representations from the encoder. We compute the probability of a word as follows:
where is a matrix to convert a hidden state to the same dimension as word vector , and is an output hidden state of decoder at word step
by using gated recurrent unit (GRU). We use the special token
Dynamic Modal Attention (DMA)
DMA takes representation vectors given by AMMT as input, and outputs the best-integrated features at each decoding step on the basis of attention on different types of representations.
Given representations from AMMT, DMA takes the form of the weighted sum of each representation as follows.
where , are the linear transformations for adjusting the dimensions so that all representation vectors have the same length, and are the weights of each representation at word step .
There are multiple choices for the design of including simple softmax, Gumbel softmax (Jang et al., 2017), straight through (ST) Gumbel softmax (Jang et al., 2017), those with temperature parameters, and weighted variants of those designs to incorporate some preference on a certain type of representation. Here we define the general form of these variants as follows, and give the ablation study in Section 5.3.
where in softmax, and in (ST) Gumbel softmax.
is the uniform distribution. When we use ST Gumbel softmax, we can transforminto a one-hot vector in the forward pass by , and approximate by in the backward pass to enable back-propagation. is the temperature to control the “hardness” of the attention. A smaller tends to give “harder” attention, which results in making an output vector close to a one-hot one. are defined as follows.
where are the weights that represent the preference for each representation. are defined as follows.
where . Note that we calculate the attention at each word step , which means that DMA can dynamically and flexibly change the attention at each word step in a sentence depending on the context. See Section 5.3 for the ablation study on the different choices of , , and .
|LSTM-YT (Venugopalan et al., 2015b)||52.9||45.8||39.8||34.6||30.7||24.8||292.9||0.423|
|S2VT (Venugopalan et al., 2015a)||64.5||58.0||52.7||48.5||45.6||33.8||428.9||0.557|
|ABiViRNet (Bolaños et al., 2018)||62.4||55.6||50.3||45.9||42.8||32.4||419.4||0.557|
|BA (Baraldi et al., 2017)||66.8||60.5||55.6||51.6||48.8||35.8||460.3||0.597|
|VTransformer (Zhou et al., 2018)||67.9||61.8||56.7||52.5||49.6||36.5||471.4||0.625|
|Ours (V+S fixed)||67.6||61.5||56.6||52.6||49.8||36.5||476.9||0.613|
|Ours (V+S dynamic)||68.4||62.3||57.4||53.5||50.7||37.2||484.8||0.618|
Captioning performance on MMAC Captions dataset. V indicates video data and S are sensor data. B-N represents BLEU score at N-gram. We computed the averaged value of each metric with six training runs.
We evaluated our approach on the MMAC Captions dataset. We split the dataset into training, validation, and test sets so that each set includes all recipe types, resulting in 2,923, 838, and 1,241 data for the training, validation, and test sets, respectively.
We set the maximum step for video frames ( for ) and IMU signals ( for ) to be 80 and 240, respectively. If the number of video frames did not match , we down-sampled or duplicated the video frame. We also down-sampled IMU signals to match .
We used the VGG16 model pre-trained with ImageNet for. The representations in the encoder had the following dimensions: , , and . Training was conducted by minimizing the cross-entropy loss with the Adam optimizer. We set the batch size to 100 sequences and the learning rates to (0 epoch 300), (epoch 400), and (400 epoch). The maximum number of words in the generated sentence, i.e., in the decoder, were set to 15. We used Gumbel softmax for Eq. (5), and set and unless otherwise stated. During training, we used scheduled sampling (Bengio et al., 2015)
, which improves generalization performance. PyTorch was used for the implementation.
Metrics and Baselines
We used four metrics for the evaluation: BLEU, METEOR, CIDEr-D, and SPICE. BLEU is a precision metric of word n-grams between prediction and ground-truth sentence. METEOR is a more semantic metric that absorbs subtle differences in expression using a WordNet synonym. CIDEr-D measures the cosine similarity between a generated sentence and ground-truth sentence using term-frequency inverse-document-frequency and avoids the gaming effect with a stemming technique. SPICE is a metric that has shown correlations with human judgement. As in most previous studies, we used METEOR and CIDEr-D as the main metrics. They are computed using the MS-COCO caption evaluation tool(Chen et al., 2015). We used average metrics of 6-times training to avoid the effect of random initialization.
We chose reasonable, versatile baselines to verify the performance of the novel multi-modal captioning task:
Table 1 lists the results on the MMAC Captions dataset. The proposed approach, which dynamically integrates vision and sensor data (Ours (V+S dynamic)) outperformed all the baselines. It achieved +1.4 gain in METEOR compared with the vision-only result (Ours (V)). As METEOR is close to a subjective evaluation, the results justify our claim that sensor-augmented multi-modal captioning is effective for generating detailed activity descriptions. Our approach when using only the sensors (Ours (S)) was inferior to the other baselines, but interestingly, it had comparable performance to LSTM-YT. This result also supports the effectiveness of sensor information. Ours (V+S fixed) is the result obtained by using all the time without using AMMT and DMA. Although it outperformed both of the single-modality variants (Ours (V) and Ours (S)) as well as achieved comparable performance to the best baseline model (VTransformer), our proposed approach with AMMT and DMA (Ours (V+S dynamic)) acquired an additional gain of 0.7 in METEOR. This result indicates that it is important to dynamically change the attention to the different modalities, and DMA effectively worked from this perspective.
Qualitative results are shown in Figure 4. In each example, the top row is a snapshot of video frames. The middle row represents 21-dimensional gyro signals (3-axes by seven locations) arranged in chronological order. The bottom row shows the captioning results. We observed that the first words, (i.e., verbs), are always generated by attending to a multi-modal representation , confirming that sensor-augmented representation is especially effective for generating verbs.
Figures 4 (a)–(b) indicate that sensor fusion helps infer the appropriate verbs and sometimes even nouns. While it is difficult for vision-only methods to distinguish visually similar actions such as Opening and Taking in these examples, the auxiliary sensor information was able to help generate the precise descriptions. Figures 4 (c)–(d) show the effectiveness of our DMA module. The colors of the generated words indicate that DMA appropriately shifts the attention from V+S to V and vice versa when necessary, resulting in better captioning results. As seen in these examples, it sometimes attends to V rather than V+S when generating nouns. We believe this is reasonable because visual information should be especially important for nouns and noisy sensor information sometimes harms the performance, although it is generally helpful for generating verbs. We will further study these points in the final paragraph of this section, referring to Figure 6. Figure 4 (e) represents a failure case where visual information alone is sufficient probably because the object is clear. In this case, sensor information caused some inaccurate inference (although it seems somewhat reasonable), but DMA failed to attend to V. Figure 4 (f) is a challenging case, where an activity took place in the out-of-camera-range. In this case, all the methods including the proposed method failed to accurately generate the description. Generating an accurate description even in such a case would require the incorporation of much broader contexts in the long term, which will be our future work, potentially by exploiting transformer-like architecture.
5.3. Further Analysis
We conducted an ablation study to investigate the characteristics of the proposed sensor-fusion method. In particular, we tested the following combinations: (i) vision: using only visual information, (ii) vision+sensor: multi-modal fusion using simple concatenation, (iii) asymmetric fusion: using AMMT for multi-modal fusion, (iv) dynamic attention: using DMA for sentence generation, and (v) full: using all the techniques.
As shown in Table 2, vision+sensor outperformed vision, which suggests the effectiveness of the sensor modality as auxiliary information. We found fusion with only DMA without AMMT (iv) were worse than fusion with simple concatenation (ii). We assume this is because it is important to adjust the different representations, i.e., , , and , so that a possible imbalance among these representations can be calibrated before being input to DMA. Similarly, fusion with only AMMT (iii) resulted in a worse performance than fusion with simple concatenation (ii) probably because the transformation layer is unnecessary if DMA is not used, and the unnecessary redundant layer caused over-fitting. Finally, full, which uses both of AMMT and DMA, led to the best results.
Analysis of AMMT
We analyzed the design of AMMT in more detail. We conducted the ablation study by changing the symmetric and asymmetric patterns to see how the fusion module achieves the gain. The results presented in Table 3 confirm the effectiveness of the asymmetric transformation. We assume this is because the possible imbalance due to the difference between visual and sensor representation is mitigated by applying a transformation. Introducing symmetric modulation, or applying transformations to both of the representations, decreased the performance probably because of the redundant parameters. Incorporating redundant parameters, despite being sufficient to apply a transformation to either of the representations, may lead to over-fitting. In contrast, both the asymmetric modulations improve the performance, especially when the transformation is applied to the sensor representations. One possible hypothesis is that the sensor data sometimes contain undesirable noise, which can be surpassed by applying a transformation.
Analysis of DMA
As mentioned in Section 4.2, we explore the variants of ; softmax with temperature (Hinton et al., 2015) and (ST) Gumbel softmax (Jang et al., 2017), namely, in softmax, and in (ST) Gumbel softmax in Eq. (5).
Table 4 shows the results. We found that Gumbel softmax worked best. We assume this is because the stochastic sampling had a similar effect as ensembling and led to a slightly better performance. ST Gumbel softmax was found to be worse than the vanilla Gumbel softmax, which indicates that it is better to use a soft attention rather than a hard one. However, ST Gumbel softmax would be a good choice if it is desirable to completely switch off certain modalities, e.g., for reducing sensor, computation, or memory cost.
Figure 5 (a) shows the sensitivity of hyper-parameter . We found that introducing a slightly hard attention by gave the best performance, whereas a too hard attention, namely , degraded the performance. This result agrees with the aforementioned observation on Gumbel and ST Gumbel softmax.
Figure 5 (b) shows the ablation for hyper-parameter ; a modality preference. We set . The best performance was obtained on . The result suggests that it is better to slightly emphasize V+S over single modalities, which indicates the effectiveness of multi-modal information.
|Softmax w/ temperature||67.9||52.8||36.5||474.8||0.612|
|ST Gumbel softmax||67.8||52.8||36.7||473.2||0.612|
Detailed Analysis on Word-types
Finally, we analyze the relationship between the generated word-types and modal attention. Figure 6 (a) shows which modalities were attended to most, or , by word-types. We can see that was attended most in most cases. In particular, was utilized for generating verbs in 99.6% of all the cases, suggesting the effectiveness of multi-modal information for describing motions. In addition, we confirmed that the visual representations were emphasized more often when generating nouns, prepositions, and determiners, with rates ranging from 26.2% to 35.3%. This indicates that sensor data are not always useful and it is better to focus only on visual representation if sensor data do not contain useful information or contain harmful information such as noise or signals of irrelevant motions. Figure 6 (b) shows a scatter plot of the average attention rate of the modalities with respect to each noun words. The upper left region means that was almost always attended to most for generating the nouns in this region, and the lower right region means was used more often to generate the nouns in this region. We found that easily identifiable objects with only vision data, e.g., plate, tended to be in the lower right region, while confusing objects, e.g., ingredients, tended to be in the upper left. The result indicates that DMA worked reasonably from a qualitative perspective as well.
This paper proposed a novel task of egocentric multi-modal captioning, which incorporates wearable sensors to generate detailed activity description. To address this task, we constructed a dataset called MMAC Captions. We proposed the model for sensor-augmented video captioning, which has two key modules; AMMT and DMA. Experimental results demonstrated that AMMT enabled to effectively integrate multi-modal representations, DMA gave more precise captioning by appropriately attending to the preferable modalities, and the combination of these two resulted in consistently superior performance compared with the baselines. We believe this study will facilitate the further research and open up an opportunity for developing new applications such as automatic generation of detailed industrial-operation-manuals.
Acknowledgements.The CMU-MMAC data used in this paper was obtained from kitchen.cs.cmu.edu and the data collection was funded in part by the National Science Foundation under Grant No. EEEC-0540865.
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