Action anticipation remains a key challenge in computer vision. However, we humans can easily address the task of “looking into the near future”. Consider the example in Fig.1, given a video clip shortly before the start of an action, we can easily predict what is going to happen next, e.g., the person will take the salt canister. More interestingly, without seeing future frames, we can vividly imagine the details of how the person will perform the action, e.g., the trajectory of the hand when reaching the canister and the part of the canister to grasp. In fact, our remarkable ability to forecast other individuals’ actions critically depends on our perception and interpretation of their body motion.
The investigation of this anticipatory mechanism can be traced back to th century. William James argued that future expectations are intrinsically related to purposive body movements 
. Additional evidence for a link between perceiving and performing actions was provided by the discovery of mirror neurons[8, 20]. The observation of others’ actions activates our motor cortex, which is the same brain region in charge of the planning and control of intentional body motion. This activation can happen even before the onset of the action and is highly correlated with the anticipation accuracy . The compelling explanation stated in  suggests that motor attention, i.e., the active prediction of meaningful future body movements, serves as a key representation for anticipation. A goal of this work is to develop a computational model for motor attention that can enable more accurate action prediction.
Despite these findings in cognitive neuroscience, the intentional body motion is largely ignored by existing action anticipation literatures [57, 11, 16, 26, 13, 15, 37, 27]. In this work, we focus on the problem of forecasting human-object interactions in First Person Vision (FPV). FPV videos capture complex hand movements during a rich set of interactions, thus providing a powerful vehicle for studying the connection between motor attention and future representation. Several previous works have investigated the problems of FPV activity anticipation [13, 15] and body movement prediction [2, 19, 12, 58]. Our work shares the same motivation of future forecasting, yet we are the first to incorporate motor attention for FPV action anticipation
To this end, we propose a novel neural network that predicts “motor attention”–the future trajectory of the hands, as an anticipatory representation of actions. Based on motor attention, our model further recognizes the type of the interaction and localizes the contact point of the interaction, i.e., interaction hotspots. Importantly, we characterize motor attention and interaction hotspots as latent variables modeled by stochastic units in the network. These units naturally deal with the uncertainty of future hand motion and human object interaction, and produce attention maps that highlight discriminative spatial-temporal features for interaction anticipation.
During inference, our model takes video clips shortly before the interaction begins as inputs, and predicts the future hand trajectory, the interaction category, and the location of the hotspots. During training, our model assumes that these outputs are available as the supervisory signals. We evaluate our model on two major FPV benchmarks: EGTEA Gaze+ and EPIC-Kitchens. We conduct detailed ablation studies to show the effectiveness of our model. Importantly, our results of action anticipation outperform the state-of-the-art by a significant margin on both datasets. At the time of submission, our results ranked the first on unseen test and the second on seen test set of EPIC-Kitchens. Moving beyond actions, we evaluate our model for hand trajectory prediction and interaction hotspot detection. Our model demonstrates strong results for both tasks. We believe that our model provides a solid step forward towards visual anticipation.
2 Related Works
There has recently been substantial interest in learning to forecast future events in videos. The most relevant works to ours are those investigations on FPV action anticipation. Our work is also related to previous studies on third person action anticipation, and some other anticipation tasks. We also review some recent efforts on visual affordance.
FPV Action Anticipation. Action anticipation indicates the task of predicting an action before it happens. We refer the readers to a recent survey  for a detailed discussion on the difference between action recognition and anticipation. The action recognition in egocentric videos has been studied extensively [48, 42, 10, 62, 35, 32, 31, 41], while fewer works target at egocentric action anticipation. Shen et al.  investigated how different egocentric modalities affect the action anticipation performance. Soran et al. 37]. Furnari et al.  introduced the task of predicting next-active objects. They used object trajectories to discriminate active objects from passive objects, and thereby predict the activated temporal segment. Their recent work  proposed to factorize the anticipation model into a “Rolling” LSTM that summarizes the past activity and an “Unrolling” LSTM that makes hypotheses of the future activity. Ke et al.  proposed a time-conditioned skip connection operation to extract relevant information from observable video sequence. In contrast to our proposed method, those prior works did not exploit the connection between human motor attention and visual perception, and did not explicitly model the contact region during human object interaction.
Third Person Action Anticipation. Several previous investigations seek to address the task of action anticipation in third person vision. Pei et al.  proposed to predict the intent of an activity by parsing video events based on a Stochastic Context Sensitive Grammar. Kris et al. 
combined semantic scene labeling with a Markov Decision Process to forecast the behavior and trajectory of the subject. Vondrick et al. proposed to predict the future video representation from large scale unlabeled video data. Felson et al.  developed a generic framework to forecast future events in sports videos. Gao et al.  proposed a Reinforced Encoder-Decoder network to summarize past frames representation and produce a hypothesis of future action. Kataoka et al.  introduced a subtle motion descriptor to identify the difference between on-going action and transitional action, and therefore facilitate future anticipation. Our work shares the same objective of future forecasting, however our goal is to exploit the abundant visual cues from egocentric videos for action anticipation.
Other Prediction Tasks. Previous works considered future anticipation under various other settings. Rhinehart et al.  proposed to an online learning algorithm to forecast the first-person trajectory. Park et al. 
proposed a fully convolutional neural network to infer possible human trajectories from egocentric stereo images. Tagi et al. addressed a novel task of predicting the future locations of an observed subject in egocentric videos. Ryoo et al.  proposed a novel method to summarize pre-activity observations for robot-centric activity prediction. An additional rich set of literatures investigated the related problem of body motion prediction. Traditional approaches [39, 56, 59] utilized state-space models to describe the body movement. Most recent works have adopted Recurrent Networks or Generative Adversarial Networks to model human dynamics. Gui et al.  introduced two global recurrent discriminators to generate human body motion. Walker et al. 12] adopted an encoder-recurrent-decoder model to jointly predict the body pose and pose label. Aksan et al.  proposed a structure prediction layer to decompose the body pose prediction into individual joints. These previous works only targeted synthetic datasets [12, 19, 2] , third person vision datasets , or less challenging FPV datasets [52, 44, 47, 61]. Most importantly, these prior works didn’t target the analysis of body movement in the context of action anticipation.
Visual affordance attracts growing interest in computer vision, since it incorporates important knowledge for scene understanding[18, 7, 60], human-object interaction recognition , and action anticipation [43, 30]. Several recent works focus on estimating visual affordances that are grounded in human object interaction. Chen et al.  proposed to estimate likely object interaction regions by learning the connection between subject and object. Fang et al.  proposed to estimate interaction regions by learning from demonstration videos. Tushar et al. 
introduced an unsupervised learning method that uses the backward attention map to approximate the interaction hotspots during an action. Both and  require a target image (a template image that contains only the active object) to infer the corresponding affordance. In contrast, our model can estimate the visual affordance in a complicated scene without any prior knowledge of active objects. In addition, those previous works did not seek to tackle the challenging problem of action anticipation.
We adopt the same egocentric action anticipation setting defined in . Assume the action segment starts at time step . Our goal is to predict the activity labels by observing a seconds video clip that precedes the action by seconds. Formally, we denote as the “anticipation time” and as the “observation time”. Given the observable video segment , we seek to predict the action label that begins at . In addition, our model also outputs the interaction hotspots at time step (the last observable frame), and motor attention from time step to time step . We refer readers to Fig. 1 for a visual illustration of our problem setting.
Fig 2 presents an overview of our model. We denote the backbone 3D convolutional network (a) as , and network features from the convolutional block as . We assume prior distribution of future hand position and interaction hotspots are available for training. However, the patterns of future hand movements and human-object interactions are highly uncertain. Even given the full video, the annotation of hand trajectory and interaction hotspots can be inaccurate. To address this challenge, we model motor attention and interaction hotspots as probabilistic variables to account for their uncertainty. Specifically, the motor attention module (b) predicts motor attention and uses stochastic units to sample from . The sampled motor attention serves an indicator of important spatial-temporal features for interaction hotspots estimation. The interaction hotspots module (c) produces interaction hotspots distribution and its sampled version . The anticipation module (d) further uses both and to aggregate network features and predicts the action label .
3.1 Deep Latent Variable Model
For our joint model, we consider motor attention and interaction hotspots as probabilistic variables, and incorporate two posteriors into our joint model:
and seem intractable at first glance. Fortunately, variational inference comes to the rescue. In the following sections, we show how the above two posteriors can be parameterized by the network. Our model has three major components:
Motor Attention Module tackles posterior . Given the network feature , our model uses the motor attention prediction function to predict motor attention .
is represented as a 3D tensor of size. Moreover, is normalized within each temporal slice, i.e., .
Interaction Hotspots Module targets at . Our model uses the interaction hotspots estimation function to estimate the interaction hotspots based on the network feature and sampled motor attention . is represented as a 2D attention map of size . A further normalization constrained that .
Anticipation Module utilizes motor attention and interaction hotspots for action anticipation. Specifically, sampled motor attention and sampled interaction hotspots are further used to aggregate feature via weighted pooling. The action anticipation function further maps the aggregated feature to future action label .
3.2 Motor Attention Module
Motor Attention Generation. The motor attention prediction function is composed of a linear function and a softmax function. maps 4D network feature into a 3D tensor. As shown in Fig 2 module (b), is implemented by 3D convolution. The softmax function further constrains this tensor to be a probabilistic distribution on each temporal slice. We denote this tensor as , which is given by . Therefore, is given by
where represents the expectation of motor attention at spatial position and time step .
Stochastic Modeling. Modeling motor attention in the context of forecasting human-object interaction requires a mechanism for addressing the stochastic nature of attention in developing the joint model. Here, we propose to use stochastic units to model the uncertainty. The key idea is to sample from the motor attention distribution. We follow the reparameterization trick introduced in [25, 36] to design a differentiable sampling mechanism:
where refers to the Gumbel distribution that can be used to sample from discrete distribution. Gumbel-Softmax can further generate a “soft” sample that allows the direct back-propagation of gradients to . is the temperature parameter, which controls the “sharpness” of the distribution. We set for all of our experiments.
3.3 Interaction Hotspots Estimation
Motor attention is used to guide interaction hotspots estimation. uses sampled motor attention , instead of , as a spatial-temporal saliency map to highlight feature representation . The interaction hotspots estimation function is composed of a linear function and a softmax function:
where denotes weighted average pooling on individual channel. Given , we can easily model the conditional probability by
where is given by Eq. 5. It represents the expectation of interaction hotspots at position .
3.4 Egocentric Action Prediction
In previous sections, we have introduced the modeling of and . The only missing piece is , which can be addressed by the action anticipation module as in Fig. 2 module (d). The action anticipation function is constructed the same way as and . Then we have
where is a linear function that predicts action labels. represents sampled interaction hotspots on the last observable frame, therefore the summation operation in Eq. 7 is only enforced on the last temporal slice of .
3.5 Training and inference
Variational Learning. Our proposed model seeks to jointly predict motor attention , interaction hotspots and the action label . Therefore, we deliberately inject posterior into and optimize the resulting latent variable model by maximizing the Evidence Lower Bound (ELBO):
|*see supplementary material for proof|
Therefore, the loss functionis given by
Comparing Eq. 7 with Eq. 3.5, we can conclude that the first term on the right hand side of Eq. 3.5 is cross entropy loss using features aggregated by motor attention and interaction hotspots . The remaining two terms enforce the model to match predicted motor attention and interaction hotspots with corresponding prior distributions. Note that we set and
as uniform distributions, when the annotation is not available for certain samples.
Approximate Inference. At inference time, our model should draw many motor attention samples and interaction hotspots samples . For high dimensional video inputs x, this process can be computationally expensive. We choose to directly feed deterministic and into Eq. 6 and Eq. 7. As introduced in the previous section, both and are convex, since they are composed of linear mapping function and softmax function. By Jensen’s Inequality:
Therefore, such approximation provides valid lower bound of and and serves as a shortcut to avoid dense sampling.
3.6 Implementation Details
Our model uses the I3D ResNet50 network with pre-trained weights from  as the backbone. We downsample all frames to with 24 fps for the EGTEA dataset, and with 30 fps for the EPIC-Kitchens dataset. We apply several data augmentation techniques, including random flipping, rotation, cropping and color jittering to avoid overfitting. Our model takes 32 consecutive frames (subsampled by 2 in the temporal dimension) as inputs, and all frames are cropped to for training. Our model is trained using SGD with momentum of 0.9 and a batch size of 64 on 4 GPUs. The initial learning rate is 0.00025 with cosine decay. We set weight decay to 1e-4 and also enable batch norm 
. Our model is implemented in PyTorch and the code will be made publicly available. For testing, our model only takes video clips with spatial resolutionas inputs, and applies spatial-temporal resampling. We then average the scores of all resampled instances to get the final prediction results
4.1 Dataset and Annotation
Dataset. We make use of two FPV benchmark datasets: EGTEA Gaze+  and Epic-Kitchens . EGTEA comes with action annotations of instances from verb classes, verb classes, and action classes. We use the first split (8,299 for training, 2,022 for testing) of the dataset to evaluate the performance of our method. EPIC-Kitchens contains 39,596 instances from 125 verbs, and 352 nouns. We follow  to split the public training set (28,472 instances) into training (23,493 instances) and validation (4,979 instances) sets, and define 2513 action classes. We conduct ablation studies on this training/validation split, and present the action anticipation results on the testing sets. For the EGTEA dataset, we set anticipation time as seconds. For the EPIC-Kitchens dataset, we set anticipation time as second as defined in the Anticipation Challenge.
|Top1 Accuracy / Mean Cls Accuracy||Top1 Accuracy / Top5 Accuracy|
|I3D||48.01 / 31.25||42.11 / 30.01||34.82 / 23.20||30.06 / 76.86||16.07 / 41.67||9.60 / 24.29|
|Soft-Atten||48.09 / 31.35||42.3 / 30.28||35.03 / 23.51||29.75 / 75.45||15.95 / 42.01||9.53 / 24.07|
|Prob-Atten||48.32 / 31.41||42.39 / 30.51||35.19 / 23.51||30.12 / 75.91||16.21 / 42.31||9.69 / 24.12|
|Ours-Det||48.58 / 32.21||43.95 / 31.26||35.69 / 23.59||30.16 / 76.86||16.25 / 41.71||9.76 / 24.40|
|Ours-MO||49.35 / 32.34||45.69 / 33.93||36.49 / 25.13||30.63 / 76.69||17.28 / 42.56||10.21 / 25.32|
|Ours||48.96 / 32.48||45.50 / 32.73||36.60 / 25.30||30.65 / 76.53||17.40 / 42.60||10.38 / 25.48|
Data Annotation. Our model requires various supervisory signals during training. We first annotate interaction hotspots on the last observable frames on EGTEA and EPIC-Kitchens datasets. Since many nouns labels in Epic-Kitchens have very few instances, we only provide interaction hotspots annotation to all many-shot nouns (defined in ) in the training data. To simplify the problem, we only consider the motor attention of one hand. The EGTEA dataset has hand mask annotation, so we use the future trajectory of fingertip that is the closest to the future active objects to represent motor attention. To mitigate the background motion, we follow 
to use optical flow and RANSAC to calculate the homography matrix and project all future finger tip positions to the last observable frame. Hand mask annotation is not available on the Epic-Kitchens dataset, so we adopt the following approach to approximate the future hand trajectory: We annotate the fingertip that is closest to the interaction hotspots on the last observable frame. Then we assign a vector pointing from the annotated fingertip to interaction hotspots, and segment this vector to approximate the motor attention. We will release all annotations. We believe those fine-grained annotations will facilitate future research in FPV human object interaction.
4.2 Evaluation Metrics
We now elaborate on our evaluation metrics for the proposed prediction tasks.
Interaction Hotspots Estimation: We downsample interaction hotspots by a spatial factor of 32 and report F1 score as in  and KL-Divergence (KLD) as in . Estimating interaction hotspots can be considered as a long-tailed binary pixel labeling problem, where each instance only has a small amount of True Positive pixels. We argue that F1 score is a more suitable metric then AUC-J, which is used by some recent affordance detection studies .
Motor Attention Prediction: We downsmaple motor attention by a spatial factor of 32 and temporal factor of 8. We consider the pixel position with highest confidence score as the predicted future fingertip position at each time slice. We then use average displacement error and final displacement error similar to previous trajectory prediction studies . We evaluate our model in pixel space, instead of real world coordinates as in .
4.3 Ablation Study
We start with an ablation study of our proposed model. Specifically, we assess the role of stochastic units, motor attention and interaction hotspots in our proposed model.
• Joint Modeling vs. I3D Backbone: We adopt the I3D  model as our backbone network. The I3D model, even if equipped with 3D convolution for temporal knowledge reasoning, performs poorly on action prediction in comparison to action recognition [31, 4]. This is because the features of the video clip preceding the action are not discriminate enough. Our model outperforms I3D network across all anticipation tasks and datasets by a large margin. The results are presented in Table 1. Our model yields major improvement on noun and action prediction ( on EGTEA and on EPIC-Kitchens).
Our model also greatly benefits interaction hotspots estimation. As shown in Table 2, our model improves the F1 score by on EGTEA and on EPIC-Kitchens. We conjecture that explicit modeling of motor attention can better localize the interaction region. This evidence suggests that explicitly modeling the connection between motor attention and visual perception can facilitate the learning of future representation. Another observation is that the performance boost on Epic-Kitchens is smaller than EGTEA-O. This is because the hand mask annotation is not available in Epic-Kitchens, and we have to approximate the future hand trajectory as discussed previously.
• Motor Attention vs. Interaction Hotspots: To better understand how motor attention and interaction hotspots contribute to the performance boost, we keep the same motor attention branch and remove the hotspots estimation branch from our model, and report the performance in Table 1 (named Ours-MO). Our-MO only lags behind our full model by a small gap on EPIC-Kitchen, and even works slightly better on EGTEA. This suggest that most of the performance boost comes from the modeling of motor attention. This again supports our claim that motor attention plays an important role in future anticipation. In contrast, interaction hotspots estimation has minor impact on action anticipation. This is because motor attention itself already includes important knowledge of interaction.
• Stochastic Modeling vs. Deterministic Modeling: The random nature of human intentional movement incurs major challenges to joint modeling. To demonstrate that our model can effectively deal with this randomness, we compare our model with its deterministic version, named Ours-Det. As shown in Table 1, modeling the motor attention in a deterministic manner can only marginally improve the performance of action anticipation. In contrast, our model outperforms its deterministic version on both action prediction and interaction hotspots estimation by a significant margin. This suggests that modeling motor attention with stochastic units can effectively account for the uncertainty in future representation like motor attention, and therefore greatly improves the performance of joint modeling.
• Attention Guided Future Anticipation: We also compare our model with attention versions of the I3D models (denoted Soft-Atten and Prob-Atten) in Table 1. The Soft-Atten model adopts the same attention generation function as our model. The attention map is used to pool network features for action anticipation. Unlike our model, Soft-Atten model does not receive any extra supervision other than action labels. The Prob-Atten model further factorizes the attention map from Soft-Atten as probabilistic variables. As studied in previous work , these attention mechanisms can effectively select salient regions, and improve FPV action recognition performance. However, the performance boost is trivial for FPV action anticipation. This suggests that visual features are not enough for future anticipation. In contrast, our model explicitly reasons about the future representation by making motor attention a first class player in our model, and thereby significantly improves the performance of action anticipation.
4.4 FPV Action Anticipation
|s1||2SCNN ||29.76 / 76.03||15.15 / 38.65||4.32 / 15.21|
|TSN ||31.81 / 76.56||16.22 / 42.15||6.00 / 18.21|
|TSN+MCE ||27.92 / 73.59||16.09 / 39.32||10.76 / 25.28|
|Trans R(2+1)D ||30.74 / 76.21||16.47 / 42.72||9.74 / 25.44|
|RULSTM ||33.04 / 79.55||22.78 / 50.95||14.39 / 33.73|
|Ours||34.99 / 77.05||20.86 / 46.45||14.04 / 31.29|
|Ours+Obj||36.25 / 79.15||23.83 / 51.98||15.42 / 34.29|
|s2||2SCNN ||25.23 / 68.66||9.97 / 27.38||2.29 / 9.35|
|TSN ||25.30 / 68.32||10.41 / 29.50||2.39 / 9.63|
|TSN+MCE ||21.27 / 63.66||9.90 / 25.50||5.57 / 25.28|
|Trans R(2+1)D ||28.37 / 69.96||12.43 / 32.20||7.24 / 19.29|
|RULSTM ||27.01 / 69.55||15.19 / 34.38||8.16 / 21.20|
|Ours||28.27 / 70.67||14.07 / 34.35||8.64 / 22.91|
|Ours+Obj||29.87 / 71.77||16.80 / 38.96||9.94 / 23.69|
We now present our experimental results for FPV action anticipation. Table 3 summarizes the performance of our model on the test sets of the EPIC-Kitchens dataset. Here, we adopt CSN model , pre-trained on a large-scale video dataset–IG-65M , as our backbone network. As shown in Table 3, our model outperforms all benchmark results from  (TSN and 2SCNN) by a large margin. Our model also performs on-par with previous state-of-the-art results (RULSTM ) on seen test set, and outperforms them on the unseen test set ( on Verb and on Action). Considering the challenging nature of the EPIC-Kitchens dataset and anticipation tasks, these performance boosts are significant. While part of the boost comes from the stronger CSN backbone, our ablation studies have already demonstrated the benefits of our proposed model.
We note that it is not possible to make a direct apples-to-apples comparison between our model and RULSTM . This is because RULSTM has access to additional object information provided by a strong object detector which was trained on the EPIC-Kitchens dataset. To bridge this gap and to further improve our results, we fuse the object stream model from . This is done by a late fusion of the prediction scores. This fused model (named Ours+Obj) achieves the best results on two test sets and across all anticipation tasks. At the time of submission, this model is ranked the first on unseen test set (and second on seen test set) on EPIC-Kitchens leaderboard.
4.5 Interaction Hotspots Estimation
We now present our experimental results on interaction hotspot estimation, and compare our method against a set of baselines, including:
• Center Prior
represents a fixed Gaussian Distribution at the center of the image.
• Grad-Cam uses the same I3D backbone network as our model, and produces a saliency map via Grad-Cam 
• EgoGaze considers possible gaze position as salient region of a given image. This model is directly trained on eye fixation annotation from EGTEA-Gaze+ .
• DSS Saliency predicts salient region during human object interaction. This model is trained on pixel-level saliency annotation from .
The experimental results are summarized in Table 2. Among all baseline methods, EgoGaze achieves the best performance on both EGTEA and EPIC-Kitchens datasets. This suggests a correlation between fixation and visual affordance, which is consistent with previous findings in the psychology literature . Even so, our model further improves the F1 score by on EGTEA and on EPIC-Kitchens. Another observation is that our model performs better on EPIC-Kitchens than EGTEA. This is because the many-shot nouns in EPIC-Kitchens are compose mostly of rigid objects. In contrast, non-rigid objects (tomato, lettuce, cucumber etc.) take up a large proportion of the EGTEA dataset, This incurs additional challenge to estimating interaction hotspots, since the interaction hotspots change dramatically as the non-rigid object itself deforms. Two recent works [9, 38] utilize template object images for predicting grounded affordances, which can significantly ease this problem. Therefore, those methods are not directly comparable with our model.
4.6 Motor Attention Prediction
We now report our experimental results on motor attention prediction. We consider the following baselines:
• Kalman Filter describes the hand trajectory prediction problem with state-space model, and assumes linear acceleration during update step.
• Gaussian Process Regression (GPR) consider the hand trajectory prediction problem as a regression problem, and iteratively predicts the future hand position.
• LSTM represents the vanilla LSTM approach for trajectory forecasting. We use the implementation from .
|Method||Avg. Disp. Error||Final Disp. Error|
Predicting the hand trajectory from the first person view is a challenging task, due to the severe ego-motion and random nature of human body movement. The experimental results are presented in Table 5
. Note that all baseline methods need the coordinate of the observed hand for prediction. This converts trajectory prediction into a regression problem. In contrast, our model only takes video clips as inputs and does not need any observation of hand position for inference. In addition, our model outputs a probability distribution which incorporates the uncertainty in human motion. Even so, our method only slightly lags behind the strongest LSTM baseline. However, LSTM encounters inevitable failure when the hand has not been observed, while our model is capable of “imagining” the possible hand trajectory. See “Operate Microwave” and “Wash Coffee Cup” in Fig3. This generalization ability is attributable to our incorporation of a latent space model of motor attention. Note that motor attention here serves a vehicle towards learning the future representation. Optimization of the performance of motor attention is a topic for future research.
4.7 Analysis and Discussion
We visualize the predicted motor attention, interaction hotspots, and action labels from our model in Fig 3. The predicted motor attention almost always attends to the predicted objects and corresponding interaction hotspots. Hence, our model can address challenging cases where next-active objects are ambiguous. Take “Operate Stove” in Fig 3 as an example. The model may predict “Put Pan” without explicitly modeling motor attention. This further supports our claim that putting motor attention into the loop results in a better future representation.
One limit of our model is that modeling motor attention as probabilistic variables cannot effectively discriminate the left hand from the right hand. We leave this piece of the puzzle for future endeavours. Our model also shares a similar conundrum faced by previous anticipation studies. The model will fail when future active objects are occluded or not even observed. (See “Close Fridge Drawer”and “Put Coffee Maker” in Fig 3
) This connects to a more general question in Artificial Intelligence: How can we endow an intelligent system with the ability of exploration and logical reasoning? The solution remains to be explored.
We have presented the first deep model that jointly predicts motor attention, interaction hotspots, and future action labels in FPV. We show that motor attention plays an important role in forecasting human-object interactions. Another key insight is that characterizing motor attention and interaction hotspots as probabilistic variables can account for the stochastic pattern of human intentional movement and human-object interaction. We obtain state-of-the-art action anticipation results on two FPV benchmark datasets, and strong results on motor attention and interaction hotspots estimation. We believe that our model connects findings in cognitive neuroscience to an important task in computer vision, thereby providing a solid step towards the challenging problem of visual anticipation.
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A Training Details and Network Architecture
In this section, we describe the training details of our model on both the EGTEA and the EPIC-Kitchens datasets. We also present the detailed network architecture. Our implementation can be found on this anonymous repository.
a.1 Implementation details
As introduced in main paper, our model takes 32 consecutive frames (subsampled by 2 in time) as the input for both EGTEA and EPIC-Kitchens datasets. This corresponds to an observation time of seconds for EGTEA and seconds for EPIC-Kitchens. We trained the model with I3D ResNet-50 backbone for epochs on the EGTEA dataset and epochs on the EPIC-Kitchens dataset. An interesting observation is that the EPIC-Kitchen dataset favors a shorter training time in comparison to the EGTEA dataset. A longer training schedule can slightly increase the mean class accuracy (though not evaluated by the EPIC-Kitchens Challenge), yet decrease the Top-1 accuracy. We train the CSN-152 model for even less epochs ( epochs) on EPIC-Kitchens training set. This is because this dense model is much easier to overfit. We thus adapt an early stopping mechanism (stops at 15 epochs) to optimize the performance on the unseen kitchens.
a.2 Network Architecture
We present our network architecture in Table 6. We use the feature from shallow layer of the network for motor attention prediction and interaction hotspots estimation. The features from shallow layer can produce attention maps with higher spatial resolutions. Our model has a similar objective as previous works on multi-task learning. The key difference is that each objective of our model is highly correlated with each other. Therefore, we do not need to re-weight the total loss based on the priority of the task. In contrast, a traditional multi-task pipeline will have to increase the weights of the cross entropy loss, if the main task is action anticipation.
B Mathematical Derivation for Equation 8
As discussed in Sec 3.5, we inject posterior into and optimize the resulting latent variable model by maximizing the Evidence Lower Bound (ELBO). However, the prior distribution of is not available for training. Here, we provide additional mathematical derivation to show that minimizing is equivalent to minimizing .
First, we have the following conditional probability:
Apparently, and are independent. Hence, we have
According to Eq. 5 in the main paper, the conditional probability can be re-written as the combination of and . Therefore, minimizing is indeed equivalent to minimizing .
C Details on Data Annotation
In Sec. 4.1, we introduced how we obtain the prior distribution of motor attention. Here we show a visual illustration of the approximation process of future hand position on the EPIC-Kitchens dataset in Fig. 4 (a). We also present more details about the interaction hotspots annotation process. An example can be found in Fig. 4 (b). For each sample, we compare the last observable frame with the first frame of action segment. If the active object presents in the last observable frame, we annotate the corresponding contact point and enforce a 2D Gaussian distribution to imitate the uncertainty of human-object interaction. If the active objects is missing from the last observable frame, we assume a uniform distribution during training. To summarize, there are annotated sample on the EPIC-Kitchens Dataset, and annotated samples on the EGTEA dataset. Note that we use a smaller anticipation time (0.5s) on the EGTEA dataset. This is because the EGTEA dataset has a smaller angle of view in comparison with the EPIC-Kitchens dataset. A large anticipation time will reduce the number of samples that have next-active objects on the last observable frame.
D Full Results on the Epic-Kitchens Dataset.
Fig 5 presents a screenshot of the leaderboard from the EPIC-Kitchens Egocentric Action Anticipation Challenge (https://epic-kitchens.github.io/). The screenshot was acquired on the end date of Phase 2 challenge (2019.11.22). To date, our proposed method outperforms all published results by a large margin. Several unpublished works (user id: “action_banks”, “reza_zlf”, “hepic”, “prefact” in Fig 5) also attempt at the EPCI-Kitchen Anticipation Challenge. On the seen kitchens (S1), “action_banks” slightly outperforms our method for action prediction, but it is inferior to our method in terms of the verb and noun prediction. On the unseen kitchens (S2), our method outperforms “action_banks” for all anticipation tasks by a notable margin.
E Additional Qualitative Results
Finally, we provide additional qualitative results. The video demo included in the supplementary materials demonstrates our results. Here we illustrate more samples of predicted motor attention, interaction hotspots, and action labels in Fig 6. The figure follows the same format as Fig. 3 in the submission. These results further show that our proposed motor attention module has the remarkable ability of “imagining” possible hand movements even without the presence of hands in the observed video segments. Another interesting observation is that the predicted distribution of interaction hostpots can be sparse in certain circumstances (e.g., “Open Fridge” or “Take Condiment”). This is because human-object interaction is a stochastic process. There might be multiple valid contact regions for manipulation, especially when the next-active object has a relatively large scale. This again shows the necessity of the stochastic units in our proposed method.
As discussed in our main paper, the occlusion and absence of active objects make the anticipation problem intractable even for humans. The failure cases in Fig. 3 also suggest that the anticipation model can be biased by on-going action. This is because current FPV datasets (especially EPIC-Kitchens) segment a continuous action into several same subatom actions to ensure all action segments have similar temporal dimension. For instance, A video clip of “cutting onions” for 20 seconds is segmented into 7 or 8 shorter clips all having the same “cutting onions” label. This increases the transition probability of staying in current state, and thereby biases the model. Therefore, the ability of predicting when exactly the action will end is important for more accurate action prediction model. This task is also related to the action localization problem in the literature.
|13||Maxpool 1||1x2x2||1x2x2||4x14x14x1||Guiding Interaction Hotspots|
|4x14x14x1||Sampling Motor Attention|
|15||Maxpool 2||1x4x4||1x4x4||4x7x7x1||Guiding Action Anticipation|
|4x7x7x1||Sampling Motor Attention|
|20||Maxpool 1||1x2x2||1x2x2||4x7x7x1||Guiding Action Anticipation|
|4x7x7x1||Sampling Interaction Hotspots|
Network architecture of our proposed model. We omit the residual connection in backbone ResNet-50 for simplification.