Using ROC and Unlabeled Data for Increasing Low-Shot Transfer Learning Classification Accuracy

One of the most important characteristics of human visual intelligence is the ability to identify unknown objects. The capability to distinguish between a substance which a human mind has no previous experience of and a familiar object, is innate to every human. In everyday life, within seconds of seeing an "unknown" object, we are able to categorize it as such without any substantial effort. Convolutional Neural Networks, regardless of how they are trained (i.e. in a conventional manner or through transfer learning) can recognize only the classes that they are trained for. When using them for classification, any candidate image will be placed in one of the available classes. We propose a low-shot classifier which can serve as the top layer to any existing CNN that the feature extractor was already trained. Using a limited amount of labeled data for the type of images which need to be specifically classified along with unlabeled data for all other images, a unique target matrix and a Receiver Operator Curve (ROC) criterion, we are able to increase identification accuracy by up to 30% for the images that do not belong to any specific classes, while retaining the ability to identify images that belong to the specific classes of interest

Using Unlabeled Data for Increasing Low-Shot Classification Accuracy of Relevant and Open-Set Irrelevant Images

In search exploration and reconnaissance tasks performed with autonomous ground vehicles an image classification capability is needed for specifically identifying targeted objects relevant classes and at the same time recognize when a candidate image does not belong to anyone of the relevant classes irrelevant images In this paper we present an open-set low-shot classifier that uses during its training a modest number less than 40 of labeled images for each relevant class and unlabeled irrelevant images that are randomly selected at each epoch of the training process The new classifier is capable of identifying images from the relevant classes determining when a candidate image is irrelevant and it can further recognize categories of irrelevant images that were not included in the training unseen The proposed low-shot classifier can be attached as a top layer to any pre-trained feature extractor when constructing a Convolutional Neural Networ

Probabilistic Deformation Models for Biometric Image Matching in Harsh Environments

This thesis demonstrates techniques for improved biometric image matching by mitigating image distortions in real world scenarios, including limited and noisy data. In particular, decreases in data quality frequently stemming from uncontrolled acquisition environments (e.g., unconstrained users), and/or distortions in the biometric patterns (due to factors such as occlusion, pose variation, or facial expression changes can significantly degrade system performance. We argue that in order to improve recognition performance in these challenging scenarios we must effectively leverage how the discrimination ability varies across the pair of images being matched. To this end we introduce several novel developments and design a newmatching framework that considers the discrimination ability of each image region at each step of matching.We argue that estimating the non-stationary deformations between the unknown image sample (referred to as the ‘probe’) and stored template (referred to as the‘gallery’) at a region-level is more robust against high-distortion matching conditions than accounting for deformations at a fine level (i.e., quantifying individualpixel shifts required to best fit one image to another). Accordingly, we introduce an unsupervised approach to automatically select said regions to use to divide the probeand gallery images based solely on discrimination ability.Using cross-correlation, the proposed model is then able to simultaneously estimate both the deformation cues (i.e., the x y translation of each region) and similarity cues (i.e., the match score for each relative shift between the corresponding regions). However, within difficult matching scenarios an authentic correlation output may be difficult to discern from an impostor output. We address this by introducing a novel approach for the implementation and design of correlation filters (CFs) as classifiers. Referred to as ’Stacked Correlation Filters’ (SCFs), this architectureconsists of training a series of stacked modular CFs with each layer refining the output of the previous layer. As previous works with CFs have only focusedon individual filter design or application, SCFs represent a new paradigm in the CF community.Finally, we employ a Bayesian graphical model to estimate the non-stationary deformations between a given probe image and gallery template and find the maximumscoring assignment for the match pair. We argue that with the derived mapping between an input comparison and output deformation score, a Gaussian ConditionalRandom Field (GCRF) can effectively capture the deformations found within the training set using a parameter estimation scheme that does not employ inference.We thoroughly analyze the performance of the proposed model via extensive experimentation on challenging data. We conduct experiments on large-scale biometricdatasets, comprised of over 62000 images from over 1000 different subjects, emulating ’in-the-wild’ matching over varying sensors, acquisition environments, andbiometric modalities. In total, we complete more than 200 million image comparisons in challenging scenarios leading to state-of-the-art verification performance

Game tree search for minimizing detectability and maximizing visibility

Abstract We introduce and study the problem of planning a trajectory for an agent to carry out a scouting mission while avoiding being detected by an adversarial opponent. This introduces a multi-objective version of classical visibility-based target search and pursuit-evasion problem. In our formulation, the agent receives a positive reward for increasing its visibility (by exploring new regions) and a negative penalty every time it is detected by the opponent. The objective is to find a finite-horizon path for the agent that balances the trade off between maximizing visibility and minimizing detectability. We model this problem as a discrete, sequential, two-player, zero-sum game. We use two types of game tree search algorithms to solve this problem: minimax search tree and Monte-Carlo search tree. Both search trees can yield the optimal policy but may require possibly exponential computational time and space. We first propose three pruning techniques to reduce the computational time while preserving optimality guarantees. When the agent and the opponent are located far from each other initially, we present a variable resolution technique with longer planning horizon to further reduce computational time. Simulation results show the effectiveness of the proposed strategies in terms of computational time

Spot Report: An Open-Source and Real-Time Secondary Task for Human-Robot Interaction User Experiments

The human-robot interaction (HRI) community is interested in a range of research questions, many of which are investigated through user experiments. Robots that occasionally require human input allow for humans to engage in secondary tasks. However, few secondary tasks transmit data in real-time and are openly available, which hinders interaction with the primary task and limits the ability of the community to build upon others’ research. Also, the need for a secondary task relevant to the military was identified by subject matter experts. To address these concerns, this paper presents the spot report task as an open-source secondary task with real-time communication for use in HRI experiments. The spot report task requires counting target objects in static images. This paper includes details of the spot report task and real-time communication with a primary task. We developed the spot report task considering the military domain, but the software architecture is domain-independent. We hope others can leverage the spot report task in their own user experiments.Automotive Research Center (ARC) by Cooperative Agreement W56HZV-19-2-0001 U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC) Warren, MI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/192466/1/HRI_LBR_2024_FINAL (1).pdfDescription of HRI_LBR_2024_FINAL (1).pdf : Final PaperSEL