Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. However, a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. To this end, we design a novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data. The proposed CDC filter performs the required temporal upsampling and spatial downsampling operations simultaneously to predict actions at the frame-level granularity. It is unique in jointly modeling action semantics in space-time and fine-grained temporal dynamics. We train the CDC network in an end-to-end manner efficiently. Our model not only achieves superior performance in detecting actions in every frame, but also significantly boosts the precision of localizing temporal boundaries. Finally, the CDC network demonstrates a very high efficiency with the ability to process 500 frames per second on a single GPU server. We will update the camera-ready version and publish the source codes online soon.
Gaidon al @cite_31 @cite_18 introduced the problem of temporally localizing actions in untrimmed videos, focusing on limited actions such as drinking and smoking'' @cite_50 and open door and sit down'' @cite_76 . Later, researchers worked on building large-scale datasets consisting of complex action categories, such as THUMOS @cite_30 @cite_12 and MEXaction2 @cite_61 @cite_69 @cite_48 , and datasets focusing on fine-grained actions @cite_27 @cite_4 @cite_16 or activities of high-level semantics @cite_49 . The typical approach used in most systems @cite_51 @cite_3 @cite_43 @cite_2 @cite_5 is extracting a pool of features, which are fed to train SVM classifiers, and then applying these classifiers on sliding windows or segment proposals for prediction. In order to design a model specific to temporal localization, Richard and Gall @cite_19 proposed using statistical length and language modeling to represent temporal and contextual structures. Heilbron al @cite_73 introduced a sparse learning framework for generating segment proposals of high recall.
@cite_30 @cite_3 @cite_43 @cite_2 @cite_5 @cite_18 @cite_69 @cite_4 @cite_48 @cite_49 @cite_19 @cite_27 @cite_50 @cite_16 @cite_76 @cite_73 @cite_12 @cite_61 @cite_31 @cite_51
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This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a proper activity list that meets users' needs. We propose to localize activities by natural language queries. Temporal Activity Localization via Language (TALL) is challenging as it requires: (1) suitable design of text and video representations to allow cross-modal matching of actions and language queries; (2) ability to locate actions accurately given features from sliding windows of limited granularity. We propose a novel Cross-modal Temporal Regression Localizer (CTRL) to jointly model text query and video clips, output alignment scores and action boundary regression results for candidate clips. For evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA. We also build complex sentence queries in Charades-STA for test. Experimental results show that CTRL outperforms previous methods significantly on both datasets. This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a proper activity list that meets users’ needs. We propose to localize activities by natural language queries. Temporal Activity Localization via Language (TALL) is challenging as it requires: (1) suitable design of text and video representations to allow cross-modal matching of actions and language queries; (2) ability to locate actions accurately given features from sliding windows of limited granularity. We propose a novel Cross-modal Temporal Regression Localizer (CTRL) to jointly model text query and video clips, output alignment scores and action boundary regression results for candidate clips. Lor evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA. We also build complex sentence queries in Charades-STA for test. Experimental results show that CTRL outperforms previous methods significantly on both datasets. 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 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 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. This paper describes a procedure for the creation of large-scale video datasets for action classification and localization from unconstrained, realistic web data. The scalability of the proposed procedure is demonstrated by building a novel video benchmark, named SLAC (Sparsely Labeled ACtions), consisting of over 520K untrimmed videos and 1.75M clip annotations spanning 200 action categories. Using our proposed framework, annotating a clip takes merely 8.8 seconds on average. This represents a saving in labeling time of over 95 compared to the traditional procedure of manual trimming and localization of actions. Our approach dramatically reduces the amount of human labeling by automatically identifying hard clips, i.e., clips that contain coherent actions but lead to prediction disagreement between action classifiers. A human annotator can disambiguate whether such a clip truly contains the hypothesized action in a handful of seconds, thus generating labels for highly informative samples at little cost. We show that our large-scale dataset can be used to effectively pre-train action recognition models, significantly improving final metrics on smaller-scale benchmarks after fine-tuning. On Kinetics, UCF-101 and HMDB-51, models pre-trained on SLAC outperform baselines trained from scratch, by 2.0 , 20.1 and 35.4 in top-1 accuracy, respectively when RGB input is used. Furthermore, we introduce a simple procedure that leverages the sparse labels in SLAC to pre-train action localization models. On THUMOS14 and ActivityNet-v1.3, our localization model improves the mAP of baseline model by 8.6 and 2.5 , respectively. 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 address temporal action 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 temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions, (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network, and (3) a localization network fine-tunes the learned classification network to localize each action instance. We propose a novel loss function for the localization network to explicitly consider temporal overlap and achieve high temporal localization accuracy. In the end, only the proposal network and the localization network are used during prediction. On two largescale 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 increases from 15.0 to 19.0 on THUMOS 2014. We address temporal action 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 temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes on the learned classification network to localize each action instance. We propose a novel loss function for the localization network to explicitly consider temporal overlap and therefore achieve high temporal localization accuracy. Only the proposal network and the localization network are used during prediction. 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 increases from 15.0 to 19.0 on THUMOS 2014, when the overlap threshold for evaluation is set to 0.5. This notebook paper describes our approaches for the action recognition and temporal localization tasks of the THUMOS Challenge 2015. For the action recognition task, we use the subsequence-score distribution (SSD) framework. We use the Improved Fisher Vectors (IFVs) encoding of the Improved Dense Trajectories (IDTs) to capture motion, as well as a VGG-16 deep net model to extract 4096 dimension feature vector to capture the context information. A linear SVM is trained for classification of 101 categories' action video clips. For the temporal localization task, we use the IFV encoding at 9 different temporal scales, and apply the above SVM to obtain a pyramid score descriptor. The score features are used for generating action labels at frame level, and by proper post processing we are able to detect the 20 class actions in given 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 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. Actions are more than just movements and trajectories: we cook to eat and we hold a cup to drink from it. A thorough understanding of videos requires going beyond appearance modeling and necessitates reasoning about the sequence of activities, as well as the higher-level constructs such as intentions. But how do we model and reason about these? We propose a fully-connected temporal CRF model for reasoning over various aspects of activities that includes objects, actions, and intentions, where the potentials are predicted by a deep network. End-to-end training of such structured models is a challenging endeavor: For inference and learning we need to construct mini-batches consisting of whole videos, leading to mini-batches with only a few videos. This causes high-correlation between data points leading to breakdown of the backprop algorithm. To address this challenge, we present an asynchronous variational inference method that allows efficient end-to-end training. Our method achieves a classification mAP of 22.4 on the Charades [42] benchmark, outperforming the state-of-the-art (17.2 mAP), and offers equal gains on the task of temporal localization. We propose a weakly-supervised structured learning approach for recognition and spatio-temporal localization of actions in video. As part of the proposed approach, we develop a generalization of the Max-Path search algorithm which allows us to efficiently search over a structured space of multiple spatio-temporal paths while also incorporating context information into the model. Instead of using spatial annotations in the form of bounding boxes to guide the latent model during training, we utilize human gaze data in the form of a weak supervisory signal. This is achieved by incorporating eye gaze, along with the classification, into the structured loss within the latent SVM learning framework. Experiments on a challenging benchmark dataset, UCF-Sports, show that our model is more accurate, in terms of classification, and achieves state-of-the-art results in localization. In addition, our model can produce top-down saliency maps conditioned on the classification label and localized latent paths. 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. Generating natural language descriptions for in-the-wild videos is a challenging task. Most state-of-the-art methods for solving this problem borrow existing deep convolutional neural network (CNN) architectures (AlexNet, GoogLeNet) to extract a visual representation of the input video. However, these deep CNN architectures are designed for single-label centered-positioned object classification. While they generate strong semantic features, they have no inherent structure allowing them to detect multiple objects of different sizes and locations in the frame. Our paper tries to solve this problem by integrating the base CNN into several fully convolutional neural networks (FCNs) to form a multi-scale network that handles multiple receptive field sizes in the original image. FCNs, previously applied to image segmentation, can generate class heat-maps efficiently compared to sliding window mechanisms, and can easily handle multiple scales. To further handle the ambiguity over multiple objects and locations, we incorporate the Multiple Instance Learning mechanism (MIL) to consider objects in different positions and at different scales simultaneously. We integrate our multi-scale multi-instance architecture with a sequence-to-sequence recurrent neural network to generate sentence descriptions based on the visual representation. Ours is the first end-to-end trainable architecture that is capable of multi-scale region processing. Evaluation on a Youtube video dataset shows the advantage of our approach compared to the original single-scale whole frame CNN model. Our flexible and efficient architecture can potentially be extended to support other video processing tasks. In this paper, we develop a new model for recognizing human actions. An action is modeled as a very sparse sequence of temporally local discriminative key frames - collections of partial key-poses of the actor(s), depicting key states in the action sequence. We cast the learning of key frames in a max-margin discriminative framework, where we treat key frames as latent variables. This allows us to (jointly) learn a set of most discriminative key frames while also learning the local temporal context between them. Key frames are encoded using a spatially-localizable pose let-like representation with HoG and BoW components learned from weak annotations, we rely on structured SVM formulation to align our components and mine for hard negatives to boost localization performance. This results in a model that supports spatio-temporal localization and is insensitive to dropped frames or partial observations. We show classification performance that is competitive with the state of the art on the benchmark UT-Interaction dataset and illustrate that our model outperforms prior methods in an on-line streaming setting. Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. However, a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. To this end, we design a novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data. The proposed CDC filter performs the required temporal upsampling and spatial downsampling operations simultaneously to predict actions at the frame-level granularity. It is unique in jointly modeling action semantics in space-time and fine-grained temporal dynamics. We train the CDC network in an end-to-end manner efficiently. Our model not only achieves superior performance in detecting actions in every frame, but also significantly boosts the precision of localizing temporal boundaries. Finally, the CDC network demonstrates a very high efficiency with the ability to process 500 frames per second on a single GPU server. Source code and trained models are available online at https: bitbucket.org columbiadvmm cdc. We investigate the feature design and classification architectures in temporal action localization. This application focuses on detecting and labeling actions in untrimmed videos, which brings more challenge than classifying presegmented videos. The major difficulty for action localization is the uncertainty of action occurrence and utilization of information from different scales. Two innovations are proposed to address this issue. First, we propose a Pyramid of Score Distribution Feature (PSDF) to capture the motion information at multiple resolutions centered at each detection window. This novel feature mitigates the influence of unknown action position and duration, and shows significant performance gain over previous detection approaches. Second, inter-frame consistency is further explored by incorporating PSDF into the state-of-the-art Recurrent Neural Networks, which gives additional performance gain in detecting actions in temporally untrimmed videos. We tested our action localization framework on the THUMOS'15 and MPII Cooking Activities Dataset, both of which show a large performance improvement over previous attempts.