494 research outputs found

    Visual perception of liquids: Insights from deep neural networks

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    Visually inferring material properties is crucial for many tasks, yet poses significant computational challenges for biological vision. Liquids and gels are particularly challenging due to their extreme variability and complex behaviour. We reasoned that measuring and modelling viscosity perception is a useful case study for identifying general principles of complex visual inferences. In recent years, artificial Deep Neural Networks (DNNs) have yielded breakthroughs in challenging real-world vision tasks. However, to model human vision, the emphasis lies not on best possible performance, but on mimicking the specific pattern of successes and errors humans make. We trained a DNN to estimate the viscosity of liquids using 100.000 simulations depicting liquids with sixteen different viscosities interacting in ten different scenes (stirring, pouring, splashing, etc). We find that a shallow feedforward network trained for only 30 epochs predicts mean observer performance better than most individual observers. This is the first successful image-computable model of human viscosity perception. Further training improved accuracy, but predicted human perception less well. We analysed the network’s features using representational similarity analysis (RSA) and a range of image descriptors (e.g. optic flow, colour saturation, GIST). This revealed clusters of units sensitive to specific classes of feature. We also find a distinct population of units that are poorly explained by hand-engineered features, but which are particularly important both for physical viscosity estimation, and for the specific pattern of human responses. The final layers represent many distinct stimulus characteristics—not just viscosity, which the network was trained on. Retraining the fully-connected layer with a reduced number of units achieves practically identical performance, but results in representations focused on viscosity, suggesting that network capacity is a crucial parameter determining whether artificial or biological neural networks use distributed vs. localized representations

    Vision for Social Robots: Human Perception and Pose Estimation

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    In order to extract the underlying meaning from a scene captured from the surrounding world in a single still image, social robots will need to learn the human ability to detect different objects, understand their arrangement and relationships relative both to their own parts and to each other, and infer the dynamics under which they are evolving. Furthermore, they will need to develop and hold a notion of context to allow assigning different meanings (semantics) to the same visual configuration (syntax) of a scene. The underlying thread of this Thesis is the investigation of new ways for enabling interactions between social robots and humans, by advancing the visual perception capabilities of robots when they process images and videos in which humans are the main focus of attention. First, we analyze the general problem of scene understanding, as social robots moving through the world need to be able to interpret scenes without having been assigned a specific preset goal. Throughout this line of research, i) we observe that human actions and interactions which can be visually discriminated from an image follow a very heavy-tailed distribution; ii) we develop an algorithm that can obtain a spatial understanding of a scene by only using cues arising from the effect of perspective on a picture of a person’s face; and iii) we define a novel taxonomy of errors for the task of estimating the 2D body pose of people in images to better explain the behavior of algorithms and highlight their underlying causes of error. Second, we focus on the specific task of 3D human pose and motion estimation from monocular 2D images using weakly supervised training data, as accurately predicting human pose will open up the possibility of richer interactions between humans and social robots. We show that when 3D ground-truth data is only available in small quantities, or not at all, it is possible to leverage knowledge about the physical properties of the human body, along with additional constraints related to alternative types of supervisory signals, to learn models that can regress the full 3D pose of the human body and predict its motions from monocular 2D images. Taken in its entirety, the intent of this Thesis is to highlight the importance of, and provide novel methodologies for, social robots' ability to interpret their surrounding environment, learn in a way that is robust to low data availability, and generalize previously observed behaviors to unknown situations in a similar way to humans.</p

    Deep Architectures for Visual Recognition and Description

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    In recent times, digital media contents are inherently of multimedia type, consisting of the form text, audio, image and video. Several of the outstanding computer Vision (CV) problems are being successfully solved with the help of modern Machine Learning (ML) techniques. Plenty of research work has already been carried out in the field of Automatic Image Annotation (AIA), Image Captioning and Video Tagging. Video Captioning, i.e., automatic description generation from digital video, however, is a different and complex problem altogether. This study compares various existing video captioning approaches available today and attempts their classification and analysis based on different parameters, viz., type of captioning methods (generation/retrieval), type of learning models employed, the desired output description length generated, etc. This dissertation also attempts to critically analyze the existing benchmark datasets used in various video captioning models and the evaluation metrics for assessing the final quality of the resultant video descriptions generated. A detailed study of important existing models, highlighting their comparative advantages as well as disadvantages are also included. In this study a novel approach for video captioning on the Microsoft Video Description (MSVD) dataset and Microsoft Video-to-Text (MSR-VTT) dataset is proposed using supervised learning techniques to train a deep combinational framework, for achieving better quality video captioning via predicting semantic tags. We develop simple shallow CNN (2D and 3D) as feature extractors, Deep Neural Networks (DNNs and Bidirectional LSTMs (BiLSTMs) as tag prediction models and Recurrent Neural Networks (RNNs) (LSTM) model as the language model. The aim of the work was to provide an alternative narrative to generating captions from videos via semantic tag predictions and deploy simpler shallower deep model architectures with lower memory requirements as solution so that it is not very memory extensive and the developed models prove to be stable and viable options when the scale of the data is increased. This study also successfully employed deep architectures like the Convolutional Neural Network (CNN) for speeding up automation process of hand gesture recognition and classification of the sign languages of the Indian classical dance form, ‘Bharatnatyam’. This hand gesture classification is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from (i) Google search engine (Google images), (ii) YouTube videos (dynamic and with background considered) and (iii) professional artists under staged environment constraints (plain backgrounds). 2) exploring the effectiveness of CNNs for identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored for achieving higher classification accuracy

    Deep filter banks for texture recognition, description, and segmentation

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    Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture representations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.Comment: 29 pages; 13 figures; 8 table

    Natural Language Description of Images and Videos

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    Understanding visual media, i.e. images and videos, has been a cornerstone topic in computer vision research for a long time. Recently, a new task within the purview of this research area, that of automatically captioning images and videos, has garnered wide-spread interest. The task involves generating a short natural language description of an image or a video. This thesis studies the automatic visual captioning problem in its entirety. A baseline visual captioning pipeline is examined, including its two constituent blocks, namely visual feature extraction and language modeling. We then discuss the challenges involved and the methods available to evaluate a visual captioning system. Building on this baseline model, several enhancements are proposed to improve the performance of both the visual feature extraction and the language modeling. Deep convolutional neural network based image features used in the baseline model are augmented with explicit object and scene detection features. In the case of videos, a combination of action recognition and static frame-level features are used. The long-short term memory network based language model used in the baseline is extended by introduction of an additional input channel and residual connections. Finally, an efficient ensembling technique based on a caption evaluator network is presented. Results from extensive experiments conducted to evaluate each of the above mentioned enhancements are reported. The image and video captioning architectures proposed in this thesis achieve state-of-the-art performance on the corresponding tasks. To support these claims, results from two video captioning challenges organized over the last year are reported, both of which were won by the models presented in the thesis. We also quantitatively analyze the automatic captions generated and identify several shortcomings of the current system. After having identified the deficiencies, we briefly look at a few interesting problems which could take the automatic visual captioning research forward
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