Action detection and temporal segmentation of actions in videos are topics of increasing interest. While fully supervised systems have gained much attention lately, full annotation of each action within the video is costly and impractical for large amounts of video data. Thus, weakly supervised action detection and temporal segmentation methods are of great importance. While most works in this area assume an ordered sequence of occurring actions to be given, our approach only uses a set of actions. Such action sets provide much less supervision since neither action ordering nor the number of action occurrences are known. In exchange, they can be easily obtained, for instance, from meta-tags, while ordered sequences still require human annotation. We introduce a system that automatically learns to temporally segment and label actions in a video, where the only supervision that is used are action sets. An evaluation on three datasets shows that our method still achieves good results although the amount of supervision is significantly smaller than for other related methods.
When processing untrimmed videos, actions can either be localized in the temporal domain only @cite_19 @cite_2 @cite_14 @cite_33 @cite_39 @cite_8 @cite_40 @cite_7 , or in the spatio-temporal domain @cite_42 @cite_46 @cite_4 @cite_25 . For the latter, videos are usually constrained to contain only few action instances. While most approaches in this area are fully supervised, @cite_25 propose a weakly supervised method for actor-action segmentation that is based on a multi-task ranking model.
@cite_14 @cite_4 @cite_33 @cite_7 @cite_8 @cite_42 @cite_39 @cite_19 @cite_40 @cite_2 @cite_46 @cite_25
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We are given a set of video clips, each one annotated with an ordered list of actions, such as “walk” then “sit” then “answer phone” extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies. We address action temporal localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained and contain multiple action instances plus video content of background scenes or other activities. To address this challenging issue, we exploit the effectiveness of deep networks in action temporal localization via multi-stage segment-based 3D ConvNets: (1) a proposal stage identifies candidate segments in a long video that may contain actions; (2) a classification stage learns one-vs-all action classification model to serve as initialization for the localization stage; and (3) a localization stage fine-tunes on the model learnt in the classification stage to localize each action instance. We propose a novel loss function for the localization stage to explicitly consider temporal overlap and therefore achieve high temporal localization accuracy. On two large-scale benchmarks, our approach achieves significantly superior performances compared with other state-of-the-art systems: mAP increases from 1.7 to 7.4 on MEXaction2 and increased from 15.0 to 19.0 on THUMOS 2014, when the overlap threshold for evaluation is set to 0.5. We are given a set of video clips, each one annotated with an ordered list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies. This paper tackles the problem of spatio-temporal action localization in a video, without assuming the availability of multiple videos or any prior annotations. Action is localized by employing images downloaded from internet using action name. Given web images, we first dampen image noise using random walk and evade distracting backgrounds within images using image action proposals. Then, given a video, we generate multiple spatio-temporal action proposals. We suppress camera and background generated proposals by exploiting optical flow gradients within proposals. To obtain the most action representative proposals, we propose to reconstruct action proposals in the video by leveraging the action proposals in images. Moreover, we preserve the temporal smoothness of the video and reconstruct all proposal bounding boxes jointly using the constraints that push the coefficients for each bounding box toward a common consensus, thus enforcing the coefficient similarity across multiple frames. We solve this optimization problem using variant of two-metric projection algorithm. Finally, the video proposal that has the lowest reconstruction cost and is motion salient is used to localize the action. Our method is not only applicable to the trimmed videos, but it can also be used for action localization in untrimmed videos, which is a very challenging problem. We present extensive experiments on trimmed as well as untrimmed datasets to validate the effectiveness of the proposed approach. This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 437 15-minute video clips, where actions are localized in space and time, resulting in 1.59M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.8 mAP, underscoring the need for developing new approaches for video understanding. This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1.58M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. We will release the dataset publicly. AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.6 mAP, underscoring the need for developing new approaches for video understanding. Spatio-temporal action detection in videos is typically addressed in a fully-supervised setup with manual annotation of training videos required at every frame. Since such annotation is extremely tedious and prohibits scalability, there is a clear need to minimize the amount of manual supervision. In this work we propose a unifying framework that can handle and combine varying types of less-demanding weak supervision. Our model is based on discriminative clustering and integrates different types of supervision as constraints on the optimization. We investigate applications of such a model to training setups with alternative supervisory signals ranging from video-level class labels over temporal points or sparse action bounding boxes to the full per-frame annotation of action bounding boxes. Experiments on the challenging UCF101-24 and DALY datasets demonstrate competitive performance of our method at a fraction of supervision used by previous methods. The flexibility of our model enables joint learning from data with different levels of annotation. Experimental results demonstrate a significant gain by adding a few fully supervised examples to otherwise weakly labeled videos. We propose a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks. Our algorithm learns from video-level class labels and predicts temporal intervals of human actions with no requirement of temporal localization annotations. We design our network to identify a sparse subset of key segments associated with target actions in a video using an attention module and fuse the key segments through adaptive temporal pooling. Our loss function is comprised of two terms that minimize the video-level action classification error and enforce the sparsity of the segment selection. At inference time, we extract and score temporal proposals using temporal class activations and class-agnostic attentions to estimate the time intervals that correspond to target actions. The proposed algorithm attains state-of-the-art results on the THUMOS14 dataset and outstanding performance on ActivityNet1.3 even with its weak supervision. We propose a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks. Our algorithm predicts temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. This objective is achieved by proposing a novel deep neural network that recognizes actions and identifies a sparse set of key segments associated with the actions through adaptive temporal pooling of video segments. We design the loss function of the network to comprise two terms--one for classification error and the other for sparsity of the selected segments. After recognizing actions with sparse attention weights for key segments, we extract temporal proposals for actions using temporal class activation mappings to estimate time intervals that localize target actions. The proposed algorithm attains state-of-the-art accuracy on the THUMOS14 dataset and outstanding performance on ActivityNet1.3 even with weak supervision. This paper is the first to address the problem of unsupervised action localization in videos. Given unlabeled data without bounding box annotations, we propose a novel approach that: 1) Discovers action class labels and 2) Spatio-temporally localizes actions in videos. It begins by computing local video features to apply spectral clustering on a set of unlabeled training videos. For each cluster of videos, an undirected graph is constructed to extract a dominant set, which are known for high internal homogeneity and in-homogeneity between vertices outside it. Next, a discriminative clustering approach is applied, by training a classifier for each cluster, to iteratively select videos from the non-dominant set and obtain complete video action classes. Once classes are discovered, training videos within each cluster are selected to perform automatic spatio-temporal annotations, by first over-segmenting videos in each discovered class into supervoxels and constructing a directed graph to apply a variant of knapsack problem with temporal constraints. Knapsack optimization jointly collects a subset of supervoxels, by enforcing the annotated action to be spatio-temporally connected and its volume to be the size of an actor. These annotations are used to train SVM action classifiers. During testing, actions are localized using a similar Knapsack approach, where supervoxels are grouped together and SVM, learned using videos from discovered action classes, is used to recognize these actions. We evaluate our approach on UCF-Sports, Sub-JHMDB, JHMDB, THUMOS13 and UCF101 datasets. Our experiments suggest that despite using no action class labels and no bounding box annotations, we are able to get competitive results to the state-of-the-art supervised methods. Temporal Action Localization (TAL) in untrimmed video is important for many applications. But it is very expensive to annotate the segment-level ground truth (action class and temporal boundary). This raises the interest of addressing TAL with weak supervision, namely only video-level annotations are available during training). However, the state-of-the-art weakly-supervised TAL methods only focus on generating good Class Activation Sequence (CAS) over time but conduct simple thresholding on CAS to localize actions. In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance. We propose a novel Outer-Inner-Contrastive (OIC) loss to automatically discover the needed segment-level supervision for training such a boundary predictor. Our method achieves dramatically improved performance: under the IoU threshold 0.5, our method improves mAP on THUMOS’14 from 13.7 to 21.2 and mAP on ActivityNet from 7.4 to 27.3 . It is also very encouraging to see that our weakly-supervised method achieves comparable results with some fully-supervised methods. We introduce an approach for spatio-temporal human action localization using sparse spatial supervision. Our method leverages the large amount of annotated humans available today and extracts human tubes by combining a state-of-the-art human detector with a tracking-by-detection approach. Given these high-quality human tubes and temporal supervision, we select positive and negative tubes with very sparse spatial supervision, i.e., only one spatially annotated frame per instance. The selected tubes allow us to effectively learn a spatio-temporal action detector based on dense trajectories or CNNs. We conduct experiments on existing action localization benchmarks: UCF-Sports, J-HMDB and UCF-101. Our results show that our approach, despite using sparse spatial supervision, performs on par with methods using full supervision, i.e., one bounding box annotation per frame. To further validate our method, we introduce DALY (Daily Action Localization in YouTube), a dataset for realistic action localization in space and time. It contains high quality temporal and spatial annotations for 3.6k instances of 10 actions in 31 hours of videos (3.3M frames). It is an order of magnitude larger than existing datasets, with more diversity in appearance and long untrimmed videos.