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| \begin{document} | |
| \title{Visual Semantic Role Labeling} | |
| \author{Saurabh Gupta \\ UC Berkeley \\ {\tt\small sgupta@eecs.berkeley.edu} | |
| \and Jitendra Malik \\ UC Berkeley \\ {\tt\small malik@eecs.berkeley.edu} } | |
| \maketitle | |
| \begin{abstract} In this paper we introduce the problem of Visual Semantic Role | |
| Labeling: given an image we want to detect people doing actions and localize | |
| the objects of interaction. Classical approaches to action recognition either | |
| study the task of action classification at the image or video clip level or at | |
| best produce a bounding box around the person doing the action. We believe such | |
| an output is inadequate and a complete understanding can only come when we are | |
| able to associate objects in the scene to the different semantic roles of the | |
| action. To enable progress towards this goal, we annotate a dataset of 16K | |
| people instances in 10K images with actions they are doing and associate | |
| objects in the scene with different semantic roles for each action. Finally, we | |
| provide a set of baseline algorithms for this task and analyze error modes | |
| providing directions for future work. \end{abstract} | |
| \section{Introduction} | |
| Current state of the art on action recognition consists of classifying a video | |
| clip containing the action, or marking a bounding box around the approximate | |
| location of the agent doing the action. Most current action recognition | |
| datasets classify each person into doing one of $k$ different activities and | |
| focus on coarse activities (like `playing baseball', `cooking', `gardening'). | |
| We argue that such a coarse understanding is incomplete and a complete visual | |
| understanding of an activity can only come when we can reason about fine | |
| grained actions constituting each such activity (like `hitting' the ball with | |
| a bat, `chopping' onions with a knife, `mowing' the lawn with a a lawn mower), | |
| reason about people doing multiple such actions at the same time, and are able | |
| to associate objects in the scene to the different semantic roles for each of these | |
| actions. | |
| \begin{figure}[t] \insertA{0.48}{figures/baseball.jpg} \caption{\small | |
| \textbf{Visual Semantic Role Labeling}: We want to go beyond classifying the | |
| action occurring in the image to being able to localize the agent, and the | |
| objects in various semantic roles associated with the action.} | |
| \figlabel{visual-srl} \end{figure} | |
| \figref{visual-srl} shows our desired output. We want to go | |
| beyond coarse activity labels such as `playing baseball', and be able to reason | |
| about fine-grained actions such as `hitting' and detect the various | |
| semantic roles for this action namely: the agent (pink box), the instrument | |
| (blue box) and the object (orange box). Such an output can help us answer | |
| various questions about the image. It tells us more about the current state of | |
| the scene depicted in the image (association of objects in the image with each | |
| other and with actions happening in the image), helps us better predict the | |
| future (the ball will leave the image from the left edge of the image, the | |
| baseball bat will swing clockwise), help us to learn commonsense about the | |
| world (naive physics, that a bat hitting a ball impacts momentum), and in turn | |
| help us in understanding `activities' (a baseball game is an outdoor sport | |
| played in a field and involves hitting a round ball with a long cylindrical | |
| bat). | |
| We call this problem as `Visual Semantic Role Labeling'. Semantic Role Labeling | |
| in a Natural Language Processing context refers to labeling words in a sentence | |
| with different semantic roles for the verb in the sentence | |
| \cite{carreras2005introduction}. NLP research on this and related areas has | |
| resulted in FrameNet~\cite{baker1998berkeley} and | |
| VerbNet~\cite{KipperSchuler2006} which catalogue verbs and their semantic | |
| roles. What is missing from such catalogues is visual grounding. Our work here | |
| strives to achieve this grounding of verbs and their various semantic roles to | |
| images. The set of actions we study along with the various roles are listed in | |
| \tableref{list}. | |
| \begin{figure*} | |
| \centering | |
| \renewcommand{\arraystretch}{0.5} | |
| \setlength{\tabcolsep}{1.0pt} | |
| \insertB{0.15}{figures/people/2_1752195.jpg} | |
| \insertB{0.15}{figures/people/2_2151556.jpg} | |
| \insertB{0.15}{figures/people/2_2154597.jpg} | |
| \insertB{0.15}{figures/people/3_424383.jpg} | |
| \insertB{0.15}{figures/people/2_2157357.jpg} | |
| \insertB{0.15}{figures/people/2_2151818.jpg} | |
| \insertB{0.15}{figures/people/3_191596.jpg} | |
| \insertB{0.15}{figures/people/3_478163.jpg} | |
| \insertB{0.15}{figures/people/2_2151213.jpg} | |
| \insertB{0.15}{figures/people/2_2155536.jpg} | |
| \insertB{0.15}{figures/people/3_192398.jpg} | |
| \insertB{0.15}{figures/people/2_485273.jpg} | |
| \insertB{0.15}{figures/people/2_483755.jpg} | |
| \insertB{0.15}{figures/people/3_453288.jpg} | |
| \insertB{0.15}{figures/people/3_492607.jpg} | |
| \insertB{0.15}{figures/people/3_475491.jpg} | |
| \insertB{0.15}{figures/people/3_465597.jpg} | |
| \insertB{0.15}{figures/people/2_2151551.jpg} | |
| \insertB{0.15}{figures/people/2_490595.jpg} | |
| \insertB{0.15}{figures/people/2_489031.jpg} | |
| \caption{\textbf{Visualizations of the images in the dataset}: We show examples | |
| of annotations in the dataset. We show the agent in the blue box and objects in | |
| various semantic roles in red. People can be doing multiple actions at the same | |
| time. First row shows: person skateboarding, person sitting and riding on horse, person | |
| sitting on chair and riding on elephant, person drinking from a glass and | |
| sitting on a chair, person sitting on a chair eating a doughnut.} | |
| \figlabel{dataset-vis} | |
| \end{figure*} | |
| Visual semantic role labeling is a new task which has not been studied before; | |
| thus we start by annotating a dataset of 16K people instances with action | |
| labels from 26 different action classes and associating objects in various | |
| semantic roles for each person labeled with a particular action. We do this | |
| annotation on the challenging Microsoft COCO (Common Objects in COntext) | |
| dataset \cite{mscoco}, which contains a wide variety of objects in complex and | |
| cluttered scenes. \figref{dataset-vis} shows some examples from our dataset. | |
| Unlike most existing datasets which either have objects or actions labeled, as | |
| a result of our annotation effort, \coco now has detailed action labels in | |
| addition to the detailed object instance segmentations, and we believe will | |
| form an interesting test bed for studying related problems. We also provide | |
| baseline algorithms for addressing this task using CNN based object detectors, | |
| and provide a discussion on future directions of research. | |
| \section{Related Work} | |
| \seclabel{related} | |
| There has been a lot of research in computer vision to understand activities and | |
| actions happening in images and videos. Here we review popular action analysis | |
| datasets, exact tasks people have studied and basic overview of techniques. | |
| PASCAL VOC \cite{PASCAL-ijcv} is one of the popular datasets for static action | |
| classification. The primary task here is to classify bounding box around people | |
| instances into 9 categories. This dataset was used in the VOC challenge. | |
| Recently, Gkioxari \etal \cite{poseactionrcnn} extended the dataset for action | |
| detection where the task is to detect and localize people doing actions. MPII | |
| Human Pose dataset is a more recent and challenging dataset for studying action | |
| \cite{andriluka14cvpr}. The MPII Human Pose dataset contains 23K images | |
| containing over 40K people with 410 different human activities. These images | |
| come from YouTube videos and in addition to the activity label also have | |
| extensive body part labels. The PASCAL dataset has enabled tremendous progress | |
| in the field of action classification, and the MPII human pose dataset has | |
| enabled studying human pose in a very principled manner, but both these | |
| datasets do not have annotations for the object of interaction which is the | |
| focus of our work here. | |
| Gupta \etal \cite{gupta2009understanding,gupta2009observing}, Yao \etal | |
| \cite{yao2011human,yao2012recognizing,yao2011classifying}, Prest \etal | |
| \cite{prest2012weakly} collect and analyze the Sports, People Playing Musical | |
| Instruments (PPMI) and the Trumpets, Bikes and Hats (TBH) datasets but the | |
| focus of these works is on modeling human pose and object context. While Gupta | |
| \etal and Yao \etal study the problem in supervised contexts, Prest \etal also | |
| propose weakly supervised methods. While these methods significantly boost | |
| performance over not using human object context and produce localization for | |
| the object of interaction as learned by their model, they do not quantify | |
| performance at the joint task of detecting people, classifying what they are | |
| doing and localizing the object of interaction. Our proposed dataset will be a | |
| natural test bed for making such quantitative measurements. | |
| There are also a large number of video datasets for activity analysis. Some of | |
| these study the task of full video action classification | |
| \cite{schuldt2004recognizing,ActionsAsSpaceTimeShapes_pami07,laptev:08,marszalek09,KarpathyCVPR14,rohrbach2012database}, | |
| while some \cite{yuan2009discriminative,Jhuang:ICCV:2013,rodriguez2008action} | |
| also study the task of detecting the agent doing the action. In particular the | |
| J-HMDB \cite{rodriguez2008action} and UCF Sports dataset | |
| \cite{Jhuang:ICCV:2013} are popular test beds for algorithms that study this | |
| task \cite{actiontubes}. More recently, \cite{rohrbach15cvpr} proposed a new | |
| video dataset where annotations come from DVS scripts. Given annotations can be | |
| generated automatically, this will be a large dataset, but is inadequate for us | |
| as it does not have the visual grounding which is our interest here. | |
| There have been a number of recent papers which generate captions for images | |
| \cite{fangCVPR15,karpathy2014deep,kelvin2015show,ryan2014multimodal,mao2014explain,vinyals2014show,kiros2014unifying,donahue2014long,chen2014learning}. | |
| Some of them also produce localization for various words that occur in the | |
| sentence \cite{fangCVPR15,kelvin2015show}. | |
| While this maybe sufficient to generate a caption for the image, the | |
| understanding is often limited only to the most salient action happening in the | |
| image (based on biases of the captions that were available for training). | |
| A caption like `A baseball match' is completely correct for the image in | |
| \figref{visual-srl}, but it is far from the detailed understanding we are striving | |
| for here: an explicit action label for each person in the image along with | |
| accurate localization for all objects in various semantic roles for the action. | |
| \section{V-COCO Dataset} In this section, we describe the Verbs in COCO | |
| (\vcoco) dataset. Our annotation process consisted of the following stages. | |
| Example images from the dataset are shown in \figref{dataset-vis}. | |
| \begin{figure}[t] | |
| \insertA{0.48}{figures/stage2.png} | |
| \insertA{0.48}{figures/stage3.png} | |
| \caption{\small | |
| \textbf{Interface for collecting annotations}: The top row shows the interface for | |
| annotating the action the person is doing, and the bottom row shows the | |
| interface for associating the roles.} | |
| \figlabel{interface} \end{figure} | |
| We build off the \coco dataset, for the following reasons, a) \coco is the most | |
| challenging object detection dataset, b) it has complete annotations for 160K | |
| images with 80 different object classes along with segmentation mask for all | |
| objects and five human written captions for each image, c) \vcoco will get | |
| richer if \coco gets richer \eg with additional annotations like human pose and | |
| key points. | |
| \textbf{Identifying verbs} | |
| The first step is to identify a set of verbs to study. We do this in a data | |
| driven manner. We use the captions in the \coco dataset and obtain a list of | |
| words for which the subject is a person (we use the Stanford dependency parser | |
| to determine the subject associated with each verb and check this subject | |
| against a list of 62 nouns and pronouns to determine if the subject is a | |
| person). We also obtain counts for each of these verbs (actions). Based on this | |
| list, we manually select a set of 30 basic verbs (actions). We picked these | |
| words with the consideration if these would be in the vocabulary of a 5-year | |
| old child. The list of verbs is tabulated in \tableref{list}. Based on | |
| visual inspection of images with these action words, we dropped \texttt{pick}, | |
| \texttt{place}, \texttt{give}, \texttt{take} because they were ambiguous from a | |
| single image. | |
| \textbf{Identifying interesting images} | |
| With this list of verbs, the next step is to identify a set of images | |
| containing people doing these actions. We do this independently for each verb. | |
| We compute two scores for each image: a) does this image have a person | |
| associated with the target verb (or its synonyms) (based on the captions for | |
| the image), b) does this image contain objects associated with the target verb | |
| (using the \coco object instance annotations, the list of associated objects | |
| was picked manually). Query expansion using the set of objects associated with | |
| the target verb was necessary to obtain enough examples. | |
| We sum these scores to obtain a ranked list of images for each verb, and | |
| consider the top 8000 images independently for each verb (in case the above | |
| two scores do not yield enough images we take additional images that contain | |
| people). We then use \amt to obtain annotations for people in these 8000 | |
| images (details on the mechanical turk annotation procedure are provided in | |
| \secref{amt}). We thus obtain a set of positive instances for each verb. The | |
| next step is to come up with a common set of images across all action | |
| categories. We do this by solving an integer program. This step of obtaining | |
| annotations separately for each action and then merging positive instances into | |
| a common pool of images was important to get enough examples for each action | |
| class. | |
| \textbf{Salient People} Given this task requires detailed reasoning (consider | |
| localizing the spoon and fork being used to \vb{eat}), instead of working with | |
| all people in the image, we work with people instances which have sufficient | |
| pixel area in the image. In addition, we also discard all people with pixel | |
| area less than half the pixel area of the largest person in the image. This | |
| helps with images which have a lot of by-standers (who may not be doing | |
| anything interesting). Doing this speeds up the annotation process | |
| significantly, and allows us to use the annotation effort more effectively. | |
| Note that we can still study the \vsrl problem in a detection setting. Given | |
| the complete annotations in \coco, even if we don't know the action that a non | |
| salient person is doing, we still know its location and appropriately adjust | |
| the training and evaluation procedures to take this into account. | |
| \textbf{Annotating salient people with all action labels} | |
| Given this set of images we annotate all `salient people' in these images with | |
| binary label for each action category. We again use \amt for obtaining these | |
| annotations but obtain annotations from 5 different workers for each person for | |
| each action. | |
| \textbf{Annotating object in various roles} | |
| Finally, we obtain annotations for objects in various roles for each action. We | |
| first enumerate the various roles for each verb, and identify object | |
| categories that are appropriate for these roles (see \tableref{list}). For | |
| each positively annotated person (with 3 or more positive votes from the | |
| previous stage) we obtain YES/NO annotation for questions of the form: | |
| `{\small Is the \textit{person} in the blue box \textit{holding} the | |
| \textit{banana} in the red box?}' | |
| \textbf{Splits} | |
| To minimize any differences in statistics between the different splits of the | |
| data, we combined 40K images from the \coco training set with the \coco | |
| validation set and obtained annotations on this joint set. After the | |
| annotation, we construct 3 splits for the \vcoco dataset: the images coming | |
| from the validation set in \coco were put into the \vcoco \textit{test} set, | |
| the rest of the images were split into \vcoco \textit{train} and \textit{val} | |
| sets. | |
| \subsection{Annotation Procedure} \seclabel{amt} In this section, we describe | |
| the Amazon Mechanical Turk (\amt) \cite{AMT} annotation procedure that we use | |
| during the various stages of dataset annotation. | |
| We follow insights from Zhou \etal \cite{zhou2014places} and use their | |
| annotation interface. We frame each annotation task as a binary {\small | |
| {YES}/{NO}} task. This has the following advantages: the user interface for | |
| such a task is simple, it is easy to insert test questions, it is easy to | |
| asses consensus, and such a task ends up getting done faster on \amt. | |
| \textbf{User Interface} We use the interface from Zhou \etal | |
| \cite{zhou2014places}. The interface is shown in \figref{interface}. We show | |
| two images: the original image on the left, and the image highlighting the | |
| person being annotated on the right. All images were marked with a default NO | |
| answer and the turker flips the answer using a key press. We inserted test | |
| questions to prevent spamming (see below) and filtered out inaccurate turkers. | |
| We composed HITs (Human Intelligence Tasks) with 450 questions (including 50 | |
| test questions) of the form: `{\small Is the person highlighted in the blue box | |
| \textit{holding} something?}' In a given HIT, the action was kept fixed. On | |
| average turkers spent 15 minutes per HIT, although this varied from action to | |
| action. For annotating the roles, an additional box was highlighted | |
| corresponding to the object, and the question was changed appropriately. | |
| \textbf{Test Questions} We had to insert test questions to ensure | |
| reliability of turker annotations. We inserted two sets of test questions. The | |
| first set was used to determine accuracy at the time of submission and this | |
| prevented turkers from submitting answers if their accuracy was too low (below | |
| 90\%). The second set of questions were used to guard from turkers who hacked | |
| the client side testing to submit incorrect results (surprisingly, we did find | |
| turkers who did this). HITs for which the average accuracy on the test set was | |
| lower than 95\% were relaunched. The set of test questions were bootstrapped | |
| from the annotation process, we started with a small set of hand labeled test | |
| questions, and enriched that set based on annotations obtained on a small set | |
| of images. We manually inspected the annotations and augmented the test set to | |
| penalize common mistakes (skiing \vs snowboarding) and excluded ambiguous | |
| examples. | |
| \subsection{Dataset Statistics} | |
| \renewcommand{\arraystretch}{1.3} | |
| \begin{table} | |
| \caption{\textbf{List of actions in \vcoco.} We list the different actions, number of | |
| semantic roles associated with the action, number of examples for each action, | |
| the different roles associated with each action along with their counts and the | |
| different objects that can be take each role. Annotations for cells marked with * are | |
| currently underway.} \vspace{2mm} | |
| \tablelabel{list} | |
| \scalebox{0.6}{ | |
| \begin{tabular}{>{\raggedright}p{0.08\textwidth}crrrp{0.35\textwidth}r} \toprule | |
| Action & Roles & \# & Role & \# & Objects in role & \\ \midrule | |
| carry & 1 & 970 & \object & * & & \\ | |
| catch & 1 & 559 & \object & 457 & sports ball, frisbee, & \\ | |
| cut & 2 & 569 & \instr & 477 & scissors, fork, knife, & \\ | |
| & & & \object & * & & \\ | |
| drink & 1 & 215 & \instr & 203 & wine glass, bottle, cup, bowl, & \\ | |
| eat & 2 & 1198 & \object & 737 & banana, apple, sandwich, orange, carrot, broccoli, hot dog, pizza, cake, donut, & \\ | |
| & & & \instr & * & & \\ | |
| hit & 2 & 716 & \instr & 657 & tennis racket, baseball bat, & \\ | |
| & & & \object & 454 & sports ball & \\ | |
| hold & 1 & 7609 & \object & * & & \\ | |
| jump & 1 & 1335 & \instr & 891 & snowboard, skis, skateboard, surfboard, & \\ | |
| kick & 1 & 322 & \object & 297 & sports ball, & \\ | |
| lay & 1 & 858 & \instr & 513 & bench, dining table, toilet, bed, couch, chair, & \\ | |
| look & 1 & 7172 & \object & * & & \\ | |
| point & 1 & 69 & \object & * & & \\ | |
| read & 1 & 227 & \object & 172 & book, & \\ | |
| ride & 1 & 1044 & \instr & 950 & bicycle, motorcycle, bus, truck, boat, train, airplane, car, horse, elephant, & \\ | |
| run & 0 & 1309 & - & - & & \\ | |
| sit & 1 & 3905 & \instr & 2161 & bicycle, motorcycle, horse, elephant, bench, chair, couch, bed, toilet, dining table, suitcase, handbag, backpack, & \\ | |
| skateboard & 1 & 906 & \instr & 869 & skateboard, & \\ | |
| ski & 1 & 924 & \instr & 797 & skis, & \\ | |
| smile & 0 & 2960 & - & - & & \\ | |
| snowboard & 1 & 665 & \instr & 628 & snowboard, & \\ | |
| stand & 0 & 8716 & - & - & & \\ | |
| surf & 1 & 984 & \instr & 949 & surfboard, & \\ | |
| talk on phone & 1 & 639 & \instr & 538 & cell phone, & \\ | |
| throw & 1 & 544 & \object & 475 & sports ball, frisbee, & \\ | |
| walk & 0 & 1253 & - & - & & \\ | |
| work on computer & 1 & 868 & \instr & 773 & laptop, & \\ | |
| \bottomrule | |
| \end{tabular}} | |
| \end{table} | |
| \begin{table} | |
| \setlength{\tabcolsep}{1.2pt} | |
| \begin{center} | |
| \caption{List and counts of actions that co-occur in \vcoco.} | |
| \tablelabel{co-occur-stats} | |
| \scriptsize{ | |
| \begin{tabular}{>{\textbf}lp{0.13\textwidth}lp{0.13\textwidth}lp{0.13\textwidth}} \toprule | |
| 597 & look, stand & & & 411 & carry, hold, stand, walk \\ | |
| 340 & hold, stand, ski & & & 329 & ride, sit \\ | |
| 324 & look, sit, work on computer & 302 & look, jump, skateboard & 296 & hold, ride, sit \\ | |
| 280 & look, surf & 269 & hold, stand & 269 & hold, look, stand \\ | |
| 259 & hold, smile, stand & 253 & stand, walk & 253 & hold, sit, eat \\ | |
| 238 & smile, stand & 230 & look, run, stand & 209 & hold, look, stand, hit \\ | |
| & & 193 & hold, smile, stand, ski & 189 & look, sit \\ | |
| 189 & look, run, stand, kick & 183 & smile, sit & & \\ | |
| 160 & look, stand, surf & 159 & hold, look, sit, eat & 152 & hold, stand, talk on phone \\ | |
| 150 & stand, snowboard & 140 & hold, look, smile, stand, cut & & \\ | |
| 129 & hold, stand, throw & 128 & look, stand, jump, skateboard & 127 & hold, smile, sit, eat \\ | |
| 124 & hold, look, stand, cut & 121 & hold, look, sit, work on computer & 117 & hold, stand, hit \\ | |
| 115 & look, jump, snowboard & 115 & look, stand, skateboard & 113 & stand, surf \\ | |
| 107 & hold, look, run, stand, hit & 105 & look, stand, throw & 104 & hold, look, stand, ski \\ | |
| \bottomrule | |
| \end{tabular}} | |
| \end{center} | |
| \end{table} | |
| In this section we list statistics on the dataset. The \vcoco | |
| dataset contains a total of 10346 images containg 16199 people | |
| instances. Each annotated person has binary labels for 26 different actions. | |
| The set of actions and the semantic roles associated with each action are | |
| listed in \tableref{list}. \tableref{list} also lists the number of positive | |
| examples for each action, the set of object categories for various roles for each | |
| ation, the number of instances with annotations for the object of interaction. | |
| We split the \vcoco dataset into a \train, \val and \test split. The \train and | |
| \val splits come from the \coco \train set while the \test set comes from the | |
| \val set. Number of images and annotated people instances in each of these | |
| splits are tabulated in \tableref{stat-splits}. | |
| Note that all images in \vcoco inherits all the annotations from the \coco | |
| dataset \cite{mscoco}, including bounding boxes for non-salient people, crowd regions, | |
| allowing us to study all tasks in a detection setting. Moreover, each image | |
| also has annotations for 80 object categories which can be used to study the | |
| role of context in such tasks. | |
| \figref{hists} (left) shows the distribution of the number of people instances | |
| in each image. Unlike past datasets which mostly have only one annotated person | |
| per image, the \vcoco dataset has a large number of images with more than one | |
| person. On average these have 1.57 people annotated with action labels per | |
| image. There are about 2000 images with two, and 800 images with three people | |
| annotated. | |
| \figref{hists} (right) shows a distribution of the number of different actions | |
| a person is doing in \vcoco. Unlike past datasets where each person can only be | |
| doing one action, people in \vcoco do on average 2.87 actions at the same time. | |
| \tableref{co-occur-stats} lists the set of actions which co-occur more than 100 | |
| times along with their counts. We also analyse human agreement for different | |
| actions to quantify the ambiguity in labeling actions from a single image by | |
| benchmarking annotations from one turker with annotations from the other | |
| turkers for each HIT for each action and produce points on precision and recall | |
| plot. \figref{human-agreement} presents these plots for the \vb{walk}, \vb{run} | |
| and \vb{surf} actions. We can see that there is high human agreement for | |
| actions like \vb{surf}, where as there is lower human agreement for verbs like | |
| \vb{walk} and \vb{run}, as expected. | |
| \begin{figure} | |
| \centering | |
| \insertA{0.23}{figures/hist_people_per_image.pdf} | |
| \insertA{0.23}{figures/hist_actions_per_person.pdf} | |
| \caption{\textbf{Statistics on \vcoco}: The bar plot on left shows the | |
| distribution of the number of annotated people per image. The bar plot on right | |
| shows the distribution of the number of actions a person is doing. Note that | |
| X-axis is on $\log$ scale.} | |
| \figlabel{hists} | |
| \end{figure} | |
| \begin{figure} | |
| \insertA{0.1492}{figures/human_agreement/walk.pdf} | |
| \insertA{0.1492}{figures/human_agreement/run.pdf} | |
| \insertA{0.1492}{figures/human_agreement/surf.pdf} | |
| \caption{Human Agreement} | |
| \figlabel{human-agreement} | |
| \end{figure} | |
| \renewcommand{\arraystretch}{1.2} | |
| \begin{table} | |
| \caption{Statistics of various splits of \vcoco.} | |
| \tablelabel{stat-splits} | |
| \begin{center} | |
| \scalebox{0.8}{ | |
| \begin{tabular}{lcccc} | |
| \toprule | |
| & \train & \val & \test & \textit{all} \\ \midrule | |
| Number of Image & 2533 & 2867 & 4946 & 10346 \\ | |
| Number of People Instance & 3932 & 4499 & 7768 & 16199 \\ \bottomrule | |
| \end{tabular}} | |
| \end{center} | |
| \end{table} | |
| \subsection{Tasks and Metrics} | |
| These annotations enable us to study a variety of new fine grained tasks about | |
| action understanding which have not been studied before. We describe these | |
| tasks below. | |
| \textbf{Agent Detection} The agent detection task is to detect instances of | |
| people engaging in a particular action. We use the standard average precision | |
| metric as used for PASCAL VOC object detection \cite{PASCAL-ijcv} to measure | |
| performance at this task - people labeled positively with the action category | |
| are treated as positive, un-annotated non-salient people are marked as difficult. | |
| \textbf{Role Detection} The role detection task is to detect the agent and | |
| the objects in the various roles for the action. An algorithm produces as | |
| output bounding boxes for the locations of the agent and each semantic role. A | |
| detection is correct if the location of the agent and each role is correct | |
| (correctness is measured using bounding box overlap as is standard). As an | |
| example, consider the role detection task for the action class `hold'. An | |
| algorithm will have to produce as output a bounding box for the person | |
| `holding', and the object being `held', and both these boxes must be correct | |
| for this detection to be correct. We follow the same precision recall | |
| philosophy and use average precision as the metric. | |
| \section{Methods} | |
| \seclabel{method} | |
| In this section, we describe the baseline approaches we investigated for | |
| studying this task. As a first step, we train object detectors for the 80 | |
| different classes in the \coco dataset. We use \rcnn \cite{girshickCVPR14} to | |
| train these detectors and use the 16-layer CNN from Simonyan and Zisserman | |
| \cite{simonyan2014very} (we denote this as \vgg). This \cnn has been shown to be | |
| very effective at a variety of tasks like object detection | |
| \cite{girshickCVPR14}, image captioning \cite{fangCVPR15}, action | |
| classification \cite{pascal_leaderboard}. We finetune this detector using the | |
| fast version of R-CNN \cite{fastrcnn} and train on 77K images from the \coco | |
| train split (we hold out the 5K \vcoco \train and \val images). We use the | |
| precomputed MCG bounding boxes from \cite{pont2015multiscale}. | |
| \paragraph{Agent detection model} Our model for agent detection starts by | |
| detecting people, and then classifies the detected people into different action | |
| categories. We train this classification model using MCG bounding boxes which | |
| have an intersection over union of more than 0.5 with the ground truth bounding | |
| box for the person. Since each person can be doing multiple actions at the same | |
| time, we frame this as a multi-label classification problem, and finetune the | |
| \vgg representation for this task. We denote this model as $A$. | |
| At test time, each person detection (after non-maximum suppression), is scored | |
| with classifiers for different actions, to obtain a probability for each | |
| action. These action probabilities are multiplied with the probability from the | |
| person detector to obtain the final score for each action class. | |
| \paragraph{Regression to bounding box for the role} Our first attempt to | |
| localize the object in semantic roles associated with an action involves | |
| training a regression model to regress to the location of the semantic role. | |
| This regression is done in the coordinate frame of the detected agent (detected | |
| using model $A$ as described above). We use the following 4 regression targets | |
| \cite{girshickCVPR14}. $(\bar{x}_{t}, \bar{y}_{t})$ denotes the center of the | |
| target box $t$, $(\bar{x}_o, \bar{y}_{o})$ denotes the center of the detected | |
| person box $o$, and $(w_{t}, h_{t})$, $(w_o, h_o)$ are the width and height of | |
| the target and person box. | |
| \begin{eqnarray} | |
| \delta(t,o) = \left(\frac{\bar{x}_{t} - \bar{x}_{o}}{w_{o}}, \frac{\bar{y}_{t} - | |
| \bar{y}_{o}}{h_{o}}, | |
| \log\left(\frac{{w}_{t}}{w_{o}}\right), \log\left(\frac{{h}_{t}}{h_{o}}\right) \right) | |
| \eqlabel{delta} | |
| \end{eqnarray} | |
| We denote this model as $B$. | |
| \paragraph{Using Object Detectors} Our second method for localizing these | |
| objects uses object detectors for the categories that can be a part of the | |
| semantic role as described in \tableref{list}. We start with the detected agent | |
| (using model $A$ above) and for each detected agent attach the highest scoring | |
| box according to the following score function: | |
| \begin{eqnarray} | |
| P_D\left(\delta(t_{c},o)\right) \times sc_{c}(t_{c}) | |
| \end{eqnarray} | |
| where $o$ refers to the box of the detected agent, box $t_c$ comes from all | |
| detection boxes for the relevant object categories $c \in \mathcal{C}$ for that | |
| action class, and $sc_c(t_c)$ refers to the detection probability for object | |
| category $c$ for box $t_{c}$. $P_D$ is the probability distribution of | |
| deformations $\delta$ computed from the training set, using the annotated agent | |
| and role boxes. We model this probability distribution using a Gaussian. The | |
| detection probabilities for different object categories $c \in \mathcal{C}$ are | |
| already calibrated using the softmax in the \fastrcnn training \cite{fastrcnn}. | |
| We denote this model as $C$. | |
| \section{Experiments} | |
| \seclabel{exp} | |
| \renewcommand{\arraystretch}{1.3} | |
| \begin{table} | |
| \centering | |
| \caption{Performance on actions in \vcoco. We report the recall for MCG | |
| candidates for objects that are part of different semantic roles for each | |
| action, AP for agent detection and role detection for 4 baselines using VGG CNN | |
| (with and without finetuning for this task). See \secref{exp} for more | |
| details.} | |
| \vspace{2mm} | |
| \tablelabel{ap} | |
| \scalebox{0.6}{ | |
| \begin{tabular}{>{\raggedleft}p{0.08\textwidth}crrrrrrrrrr} \toprule | |
| Action & Role & \multicolumn{3}{c}{MCG Recall} & & \multicolumn{5}{c}{Average Precision}\\ | |
| \cmidrule(r){3-5} \cmidrule(r){7-11} | |
| & & & & & & $A$ & $B_0$ & $B$ & $C_0$ & $C$ & \\ | |
| & & mean & R[0.5] & R[0.7] & & agent & role & role & role & role & \\ \midrule | |
| carry & \object* & & & & & 54.2 & & & & & \\ | |
| catch & \object & 73.7 & 91.6 & 67.2 & & 41.4 & 1.1 & 1.2 & 24.1 & 22.5 & \\ | |
| cut & \instr & 58.6 & 61.4 & 30.7 & & 44.5 & 1.6 & 2.3 & 4.6 & 3.9 & \\ | |
| & \object* & & & & & & & & & & \\ | |
| drink & \instr & 69.5 & 82.1 & 58.2 & & 25.1 & 0.3 & 0.7 & 3.1 & 6.4 & \\ | |
| eat & \object & 84.4 & 97.8 & 89.7 & & 70.2 & 8.0 & 11.0 & 37.0 & 46.2 & \\ | |
| & \instr* & & & & & & & & & & \\ | |
| hit & \instr & 72.0 & 88.1 & 60.5 & & 82.6 & 0.2 & 0.7 & 31.0 & 31.0 & \\ | |
| & \object & 62.0 & 73.8 & 53.3 & & & 11.3 & 11.8 & 41.3 & 44.6 & \\ | |
| hold & \object* & & & & & 73.4 & & & & & \\ | |
| jump & \instr & 76.0 & 88.7 & 68.7 & & 69.2 & 4.0 & 17.0 & 33.9 & 35.3 & \\ | |
| kick & \object & 82.7 & 100.0 & 94.4 & & 61.6 & 0.3 & 0.8 & 48.8 & 48.3 & \\ | |
| lay & \instr & 94.6 & 100.0 & 97.7 & & 39.3 & 19.9 & 28.0 & 32.8 & 34.3 & \\ | |
| look & \object* & & & & & 65.0 & & & & & \\ | |
| point & \object* & & & & & 1.4 & & & & & \\ | |
| read & \object & 83.5 & 96.2 & 82.7 & & 10.6 & 0.9 & 2.1 & 2.2 & 4.7 & \\ | |
| ride & \instr & 84.7 & 99.1 & 87.9 & & 45.4 & 1.2 & 9.7 & 12.5 & 27.6 & \\ | |
| run & - & & & & & 59.7 & & & & & \\ | |
| sit & \instr & 82.2 & 94.7 & 82.6 & & 64.1 & 20.0 & 22.3 & 24.3 & 29.2 & \\ | |
| skateboard & \instr & 73.2 & 87.3 & 63.7 & & 83.7 & 3.0 & 12.2 & 32.7 & 40.2 & \\ | |
| ski & \instr & 49.1 & 46.5 & 20.9 & & 81.9 & 4.9 & 5.5 & 5.9 & 8.2 & \\ | |
| smile & - & & & & & 61.9 & & & & & \\ | |
| snowboard & \instr & 67.8 & 75.1 & 51.7 & & 75.8 & 4.3 & 13.6 & 20.2 & 28.1 & \\ | |
| stand & - & & & & & 81.0 & & & & & \\ | |
| surf & \instr & 66.7 & 76.0 & 53.2 & & 94.0 & 1.5 & 4.8 & 28.1 & 27.3 & \\ | |
| talk on phone & \instr & 59.9 & 69.3 & 37.3 & & 46.6 & 1.1 & 0.6 & 5.8 & 5.8 & \\ | |
| throw & \object & 72.5 & 88.0 & 73.6 & & 50.1 & 0.4 & 0.5 & 25.7 & 25.4 & \\ | |
| walk & - & & & & & 56.3 & & & & & \\ | |
| work on computer & \object & 85.6 & 98.6 & 88.5 & & 56.9 & 1.4 & 4.9 & 29.8 & 32.3 & \\ \midrule | |
| mean & & 73.6 & 85.0 & 66.4 & & 57.5 & 4.5 & 7.9 & 23.4 & 26.4 & \\ \bottomrule | |
| \end{tabular}} | |
| \end{table} | |
| \begin{figure*} | |
| \centering | |
| \renewcommand{\arraystretch}{0.3} | |
| \setlength{\tabcolsep}{1.0pt} | |
| \begin{tabular}{p{0.02\textwidth}>{\raggedright\arraybackslash}p{0.98\textwidth}} | |
| \vertical{Correct} & | |
| \insertB{0.13}{figures/error_modes_ft//correct/cut_instr_tp_0041.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/eat_obj_tp_0005.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/hit_instr_tp_0013.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/kick_obj_tp_0009.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/read_obj_tp_0042.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/ride_instr_tp_0025.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/skateboard_instr_tp_0011.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/snowboard_instr_tp_0007.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/surf_instr_tp_0003.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/read_obj_tp_0011.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/talk_on_phone_instr_tp_0002.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//correct/throw_obj_tp_0002.jpg} | |
| \\ \vertical{Incorrect Class} & | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/catch_obj_wrong_label_0033.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/cut_instr_wrong_label_0036.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/hit_instr_wrong_label_0080.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/kick_obj_wrong_label_0040.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/lay_instr_wrong_label_0040.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/read_obj_wrong_label_0012.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/ride_instr_wrong_label_0034.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/snowboard_instr_wrong_label_0038.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/surf_instr_wrong_label_0150.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/talk_on_phone_instr_wrong_label_0015.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/throw_obj_wrong_label_0008.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//bad_class/work_on_computer_instr_wrong_label_0067.jpg} | |
| \\ \vertical{Mis-Grouping} & | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/cut_instr_mis_group_0023.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/drink_instr_mis_group_0056.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/eat_obj_mis_group_0031.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/hit_instr_mis_group_0078.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/jump_instr_mis_group_0178.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/ride_instr_mis_group_0205.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/sit_instr_mis_group_0097.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/skateboard_instr_mis_group_0155.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/ski_instr_mis_group_0051.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/snowboard_instr_mis_group_0147.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/work_on_computer_instr_mis_group_0015.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misgroup/work_on_computer_instr_mis_group_0045.jpg} | |
| \\ \vertical{Mis-Localization} & | |
| \insertB{0.13}{figures/error_modes_ft//misloc/cut_instr_o_misloc_0019.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misloc/drink_instr_o_misloc_0032.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misloc/lay_instr_o_misloc_0120.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misloc/ride_instr_o_misloc_0035.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//misloc/talk_on_phone_instr_o_misloc_0011.jpg} | |
| \\ \vertical{Hallucination} & | |
| \insertB{0.13}{figures/error_modes_ft//halucination/catch_obj_o_hall_0031.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//halucination/cut_instr_o_hall_0070.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//halucination/eat_obj_o_hall_0134.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//halucination/skateboard_instr_o_hall_0142.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//halucination/ski_instr_o_hall_0202.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//halucination/snowboard_instr_o_hall_0204.jpg} | |
| \insertB{0.13}{figures/error_modes_ft//halucination/talk_on_phone_instr_o_hall_0039.jpg} | |
| \\ | |
| \end{tabular} | |
| \caption{Visualizations of detections from our best performing baseline | |
| algorithm. We show the detected agent in the blue box and the detected object | |
| in the semantic role in the red box and indicate the inferred action class in | |
| the test at the bottom of the image. We show some correct detections in the top | |
| two rows, and common error modes in subsequent rows. `Incorrect Class': when | |
| the inferred action class label is wrong; `Mis-Grouping': correctly localized | |
| and semantically feasible but incorrectly matched to the agent; and | |
| `Mis-localization' and `Hallucination' of the object of interaction.} | |
| \figlabel{vis-detections} | |
| \end{figure*} | |
| We summarize our results here. We report all results on the \vcoco \val set. | |
| Since we use bounding box proposals, we analyze the recall for these proposals | |
| on the objects that are part of various semantic roles. For each semantic role | |
| for each action class, we compute the coverage (measured as the intersection | |
| over union of the best overlapping MCG bounding box with the ground truth | |
| bounding box for the object in the role) for each instance and report the | |
| mean coverage, recall at 50\% overlap and recall at 70\% overlap | |
| (\tableref{ap} columns three to five). We see reasonable recall whenever the | |
| object in the semantic role is large (\eg bed, bench for \vb{lay}, horse, | |
| elephant, train, buses for \vb{ride}) or small but highly distinctive (\eg | |
| football for \vb{kick}, doughnuts, hot dogs for \vb{eat obj}) but worse when | |
| the object can be in drastic motion (\eg tennis rackets and baseball bats for | |
| \vb{hit instr}), or small and not distinctive (\eg tennis ball for \vb{hit | |
| obj}, cell phone for \vb{talk on phone}, ski for \vb{ski}, scissors and knife | |
| for \vb{cut}). | |
| Given that our algorithms start with a person detection, we report the | |
| average precision of the person detector we are using. On the \vcoco \val set | |
| our person detector which uses the 16-layer \vgg network in the Fast R-CNN | |
| \cite{fastrcnn} framework gives an average precision of 62.54\%. | |
| We next report the performance at the task of agent detection (\tableref{ap}) | |
| using model $A$ as described in \secref{method}. We observe a mean average | |
| precision of 57.5\%. Performance is high for action classes which occur in a | |
| distinctive scene like \vb{surf} (94.0\%, occurring in water) \vb{ski}, | |
| \vb{snowboard} (81.9\% and 75.8\%, occurring in snow) and \vb{hit} (82.6\%, | |
| occurring in sports fields). Performance is also high for classes which have a | |
| distinctive object associated with the action like \vb{eat} (70.2\%). | |
| Performance for classes which are identified by an object which is not easy to | |
| identify is lower \eg wine glasses for \vb{drink} (25.1\%), books for \vb{read} | |
| (10.6\%), \eg object being cut and the instrument being used for \vb{cut} | |
| (44.5). Performance is also worse for action classes which require reasoning | |
| about large spatial relationships \eg 61.6\% for \vb{kick}, and fine grained | |
| reasoning of human pose \eg 41.4\%, 50.1\% for \vb{catch} and \vb{throw}. | |
| Finetuning the \vgg representation for this task improves performance | |
| significantly and just training a SVM on the \vgg \texttt{fc7} features | |
| (finetuned for object detection on \coco) performs much worse at 46.8\%. | |
| We now report performance of the two baseline algorithms on the role detection | |
| task. We first report performance of algorithm $B_0$ which simply pastes the box | |
| at the mean deformation location and scale (determined using the mean of the | |
| $\delta$ vector as defined in \eqref{delta} across the training set for each | |
| action class separately). This does poorly and gives a mean average precision | |
| for the role detection task of 4.5\%. Using the regression model $B$ as | |
| described in \secref{method} to predict the location and scale of the | |
| semantic role does better giving a mAP of 7.9\%, with high performing classes | |
| being \vb{sit}, and \vb{lay} for which the object of interaction is always | |
| below the person. Using object detector output from \vgg and using the | |
| location of the highest scoring object detection (from the set of relevant | |
| categories for the semantic roles for the action class) without any spatial | |
| model (denoted as $C_0$) gives a mAP of 23.4\%. Finally, model $C$ which | |
| also uses a spatial consistency term in addition to the score of the objects | |
| detected in the image performs the best among these four baseline algorithms | |
| giving a mAP of 26.4\%. Modeling the spatial relationship helps for cases | |
| when there are multiple agents in the scene doing similar things \eg | |
| performance for \vb{eat} goes up from 37.0\% to 46.2\%, for \vb{ride} goes up | |
| from 12.5\% to 27.6\%. | |
| \paragraph{Visualizations} | |
| Finally, we visualize the output from our best performing baseline algorithm in | |
| \figref{vis-detections}. We show some correct detections and various error | |
| modes. One of the common error modes is incorrect labeling of the action | |
| (\vb{ski} \vs \vb{snowboard}, \vb{catch} \vs \vb{throw}). Even with a spatial | |
| model, there is very often a mis grouping for the incorrect role with the | |
| agent. This is common when there are multiple people doing the same action in | |
| an image \eg multiple people \vb{riding} horses, or \vb{skateboarding}, or | |
| \vb{working} on a laptops. Finally, a lot of errors are also due to | |
| mis-localization and hallucination of object of interaction in particular when | |
| the object is small \eg ski for \vb{skiing}, books for \vb{reading}. | |
| \paragraph{Error Modes} | |
| Having such annotations also enables us to analyze different error modes. | |
| Following \cite{hoiem2012diagnosing}, we consider the top | |
| $num\_inst$ detections for each class ($num\_inst$ is the number of instances | |
| for that action class), and classify the false positives in these top | |
| detections into the following error modes: | |
| \begin{enumerate} | |
| \item \textbf{bck}: when the agent is detected on the background. (IU with | |
| any labeled person is less than 0.1). | |
| \item \textbf{bck person}: when the agent is detected on the background, close | |
| to people in the background. Detections on background people are not | |
| penalized, however detections which have overlap between 0.1 and 0.5 with | |
| people in the background are still penalized and this error mode computes | |
| that fraction. | |
| \item \textbf{incorrect label}: when the agent is detected around a person | |
| that is labeled to be not doing this action. | |
| \item \textbf{person misloc}: when the agent is detected around a person | |
| doing the action but is not correctly localized (IU between 0.1 and 0.5) (the | |
| object is correctly localized). | |
| \item \textbf{obj misloc}: when the object in the semantic role is not | |
| properly localized (IU between 0.1 and 0.5) (the agent is correctly | |
| localized). | |
| \item \textbf{both misloc}: when both the object and the agent are improperly | |
| localized (IU for both is between 0.1 and 0.5). | |
| \item \textbf{mis pairing}: when the object is of the correct semantic class | |
| but not in the semantic role associated with this agent. | |
| \item \textbf{obj hallucination}: when the object is detected on the | |
| background. | |
| \end{enumerate} | |
| \figref{fp_distr} shows the distribution of these errors for the 2 best | |
| performing baselines that we experimented with, model $C_0$ and $C$. | |
| The most dominant error mode for these models is incorrect classification of | |
| the action, \figref{vis-detections} shows some examples. Another error mode is | |
| mis localization of the object for categories like \vb{ski}, \vb{surf}, | |
| \vb{skateboard}, and \vb{snowboard}. This is also evident from the poor recall | |
| of the region proposals for objects categories associated with these actions. A | |
| large number of errors also come from `person misloc' for categories like | |
| \vb{lay} which is because of unusual agent pose. We also observe that the `mis | |
| pairing' errors decrease as we start modeling the deformation between the agent | |
| and the object. Finally, a large number of error for \vb{cut} and \vb{hit-obj} | |
| come from hallucinations of the object in the background. | |
| \begin{figure*} | |
| \centering | |
| \begin{subfigure}{0.48\textwidth} | |
| \insertA{1.0}{figures/analysis_ft/snap_detections_no_deform_fp_distr.png} | |
| \caption{Full Model without deformations ($C_0$)} \end{subfigure} | |
| \begin{subfigure}{0.48\textwidth} | |
| \insertA{1.0}{figures/analysis_ft/snap_detections_fp_distr.png} \caption{Full | |
| model ($C$)} \end{subfigure} | |
| \caption{Distribution of the false positives in the top $num\_inst$ detections | |
| for each action class (with roles if applicable). `bck' and `bck person' | |
| indicate when the detected agent is on background (IU with any person less | |
| than 0.1) or around people in the background (IU with background people | |
| between 0.1 and 0.5), `incorrect label' refers to when the detected agent is | |
| not doing the relevant action, `person misloc' refers to when the agent | |
| detection is mis localized, `obj misloc' refers to when the object in the | |
| specific semantic role is mis localized, `both misloc' refers to when both | |
| the agent and the object are mis localized (mis localization means the IU is | |
| between 0.1 to 0.5). Finally, `mis pairing' refers to when the object of | |
| interaction is of the correct semantic class but not in the semantic role for | |
| the detected agent, and `obj hallucination' refers to when the object of | |
| interaction is hallucinated. The first figure shows the distribution for the | |
| $C_0$ model (which does not model deformation between agent and object), | |
| and the second figure shows the distribution for model $C$ (which models | |
| deformation between the agent and the object).} | |
| \figlabel{fp_distr} | |
| \end{figure*} | |
| \paragraph{Conclusions and Future Directions} | |
| In this work, we have proposed the task of visual semantic role labeling in | |
| images. The goal of this task is to be able to detect people, classify what | |
| they are doing and localize the different objects in various semantic roles | |
| associated with the inferred action. We have collected an extensive dataset | |
| consisting of 16K people in 10K images. Each annotated person is labeled with | |
| 26 different actions labels and has been associated with different objects in | |
| the different semantic roles for each action. We have presented and analyzed | |
| the performance of four simple baseline algorithms. Our analysis shows the | |
| challenging nature of this problem and points to some natural directions of | |
| future research. We believe our proposed dataset and tasks will enable us to | |
| achieve a better understanding of actions and activities than current | |
| algorithms. | |
| \epigraph{Concepts without percepts are empty, percepts without concepts are | |
| blind.}{Immanuel Kant} | |
| \paragraph{Acknowledgments: } | |
| This work was supported by {ONR SMARTS MURI N00014-09-1-1051}, and a Berkeley | |
| Graduate Fellowship. We gratefully acknowledge {NVIDIA} corporation for the | |
| donation of Tesla and Titan GPUs used for this research. | |
| {\small | |
| \bibliographystyle{ieee} | |
| \bibliography{refs-rbg} | |
| } | |
| \end{document} | |