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Filtering by: Creator Zégre, Nicolas P. Remove constraint Creator: Zégre, Nicolas P. Committee Member Li, Fuxin Remove constraint Committee Member: Li, Fuxin

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  • Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

    Creator:
    Aziere, Nicolas
    Abstract:
    This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Nicolas Aziere for

  • Image Based Crypto

    Creator:
    Basa, Gungor
    Abstract:
    The ability to create reproducible cryptographically secure keys from temporal environments (e.g., images) has the potential to be a contributor to effective cryptographic mechanisms. Due to the noisy nature of these environments, achieving this goal in a user friendly fashion is a very challenging task, especially since there exists a...
    Resource Type:
    Masters Thesis
    Full Text:
    another position based scheme. 8 Chapter 3: Notation and Preliminaries Notation: ~f , ~b, H, P , I

  • Window Axial Vision Transformer for Image Classification

    Creator:
    Lee, Jisoo
    Abstract:
    Currently, a popular approach to image classification uses the deep Transformer architecture. In a Transformer, the attention mechanism enables the model to learn efficiently with fewer computational resources than the convolutional neural networks (CNNs). In this thesis, we study the sparse attention mechanism widely used in the Transformers developed specifically...
    Resource Type:
    Masters Thesis
    Full Text:
    , Nicolas, Tieqiao, and Ajay for giving me insight into this study and feedback. Thanks should go to Dr

  • Counterfactual States for Atari Agents via Generative Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
    Resource Type:
    Masters Thesis
    Full Text:
    ))), whereH(p) = − ∑ i pilog(pi) Therefore, the adversarial loss can be written as: LAdv = λ |X | ∑ (s,a

  • Investigating Latent State and Uncertainty Representations in Reinforcement Learning

    Creator:
    Koul, Anurag
    Abstract:
    Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
    Resource Type:
    Dissertation
    Full Text:
    Machine will correspond to the p distinct quantized states in the data and the observation-space of the

  • Action Segmentation with Limited Supervision

    Creator:
    Li, Jun
    Abstract:
    In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
    Resource Type:
    Dissertation
    Full Text:
    , Nicolas Aziere, Peng Lei, Jialin Yuan, Xingyi Li, Lawrence Neal, Neale Ratzlaff, Wenxuan Wu, Hung Nguyen

  • Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

    Creator:
    Lei, Peng
    Abstract:
    This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
    Resource Type:
    Dissertation
    Full Text:
    class labels yt = {ytp : ytp ∈ {0, 1}L} for pixels p in video frame t, where ytp has only one non-zero

  • Robust and Efficient Classification of Videos in the Wild

    Creator:
    Mahasseni, Behrooz
    Abstract:
    Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
    Resource Type:
    Dissertation

  • Graph Imitation Learning for Optimal Joint Beamforming and Antenna Selection

    Creator:
    Shrestha, Sagar
    Abstract:
    Transmit beamforming is an important technique employed to improve efficiency and signal quality in wireless communication systems by steering signals towards their in- tended users. It often arises jointly with the antenna selection problem due to various reasons, such as limited number of radio frequency (RF) chains and energy/resource effi-...
    Resource Type:
    Masters Thesis
    Full Text:
    corresponds to a branching (partitioning a node) operation. Use P(t) to denote the collection of (s, ℓ

  • Ideal Thumbnail-Preserving Encryption for Balancing Image Privacy and Usability

    Creator:
    Tajik, Kimia
    Abstract:
    In this dissertation, we propose Ideal Thumbnail-Preserving Encryption (Ideal TPE), as a special case of format-preserving encryption, to balance image privacy and usability concerns in a cloud environment. We first introduce a concrete construction for Ideal TPE, that provably leaks nothing about the plaintext (unencrypted) image beyond its thumbnail. We...
    Resource Type:
    Dissertation
    Full Text:
    instances of our problem, where calculation of P matrix and λ∗ are feasible. . . . . . . . . . . 39 5.5 TPE