Medial temporal lobe activation during encoding and retrieval of novel face-name pairs

The human medial temporal lobe (MTL) is known to be involved in declarative memory, yet the exact contributions of the various MTL structures are not well understood. In particular, the data as to whether the hippocampal region is preferentially involved in the encoding and/or retrieval of associative memory have not allowed for a consensus concerning its specific role. To investigate the role of the hippocampal region and the nearby MTL cortical areas in encoding and retrieval of associative versus non-associative memories, we used functional magnetic resonance imaging (fMRI) to measure brain activity during learning and later recognition testing of novel face-name pairs. We show that there is greater activity for successful encoding of associative information than for non-associative information in the right hippocampal region, as well as in the left amygdala and right parahippocampal cortex. Activity for retrieval of associative information was greater than for non-associative information in the right hippocampal region also, as well as in the left perirhinal cortex, right entorhinal cortex, and right parahippocampal cortex. The implications of these data for a clear functional distinction between the hippocampal region and the MTL cortical structures are discussed. © 2004 Wiley-Liss, Inc

One video is sufficient? Human activity recognition using active video composition

In this paper, we present a novel human activity recogni-tion approach that only requires a single video example per activity. We introduce the paradigm of active video com-position, which enables one-example recognition of com-plex activities. The idea is to automatically create a large number of semi-artificial training videos called composed videos by manipulating an original human activity video. A methodology to automatically compose activity videos hav-ing different backgrounds, translations, scales, actors, and movement structures is described in this paper. Further-more, an active learning algorithm to model the temporal structure of the human activity has been designed, prevent-ing the generation of composed training videos violating the structural constraints of the activity. The intention is to gen-erate composed videos having correct organizations, and take advantage of them for the training of the recognition system. In contrast to previous passive recognition systems relying only on given training videos, our methodology ac-tively composes necessary training videos that the system is expected to observe in its environment. Experimental re-sults illustrate that a single fully labeled video per activity is sufficient for our methodology to reliably recognize human activities by utilizing composed training videos. 1

Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks

Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.Comment: Accepted to IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 201

3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks

Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary version was published in ACM MM'14 conferenc

Unsupervised Learning of Long-Term Motion Dynamics for Videos

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.Comment: CVPR 201

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Action Recognition by Hierarchical Mid-level Action Elements

Realistic videos of human actions exhibit rich spatiotemporal structures at multiple levels of granularity: an action can always be decomposed into multiple finer-grained elements in both space and time. To capture this intuition, we propose to represent videos by a hierarchy of mid-level action elements (MAEs), where each MAE corresponds to an action-related spatiotemporal segment in the video. We introduce an unsupervised method to generate this representation from videos. Our method is capable of distinguishing action-related segments from background segments and representing actions at multiple spatiotemporal resolutions. Given a set of spatiotemporal segments generated from the training data, we introduce a discriminative clustering algorithm that automatically discovers MAEs at multiple levels of granularity. We develop structured models that capture a rich set of spatial, temporal and hierarchical relations among the segments, where the action label and multiple levels of MAE labels are jointly inferred. The proposed model achieves state-of-the-art performance in multiple action recognition benchmarks. Moreover, we demonstrate the effectiveness of our model in real-world applications such as action recognition in large-scale untrimmed videos and action parsing