Object-Proposal Evaluation Protocol is 'Gameable'

Object proposals have quickly become the de-facto pre-processing step in a number of vision pipelines (for object detection, object discovery, and other tasks). Their performance is usually evaluated on partially annotated datasets. In this paper, we argue that the choice of using a partially annotated dataset for evaluation of object proposals is problematic -- as we demonstrate via a thought experiment, the evaluation protocol is 'gameable', in the sense that progress under this protocol does not necessarily correspond to a "better" category independent object proposal algorithm. To alleviate this problem, we: (1) Introduce a nearly-fully annotated version of PASCAL VOC dataset, which serves as a test-bed to check if object proposal techniques are overfitting to a particular list of categories. (2) Perform an exhaustive evaluation of object proposal methods on our introduced nearly-fully annotated PASCAL dataset and perform cross-dataset generalization experiments; and (3) Introduce a diagnostic experiment to detect the bias capacity in an object proposal algorithm. This tool circumvents the need to collect a densely annotated dataset, which can be expensive and cumbersome to collect. Finally, we plan to release an easy-to-use toolbox which combines various publicly available implementations of object proposal algorithms which standardizes the proposal generation and evaluation so that new methods can be added and evaluated on different datasets. We hope that the results presented in the paper will motivate the community to test the category independence of various object proposal methods by carefully choosing the evaluation protocol.Comment: 15 pages, 11 figures, 4 table

Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks

The standard probabilistic perspective on machine learning gives rise to empirical risk-minimization tasks that are frequently solved by stochastic gradient descent (SGD) and variants thereof. We present a formulation of these tasks as classical inverse or filtering problems and, furthermore, we propose an efficient, gradient-free algorithm for finding a solution to these problems using ensemble Kalman inversion (EKI). Applications of our approach include offline and online supervised learning with deep neural networks, as well as graph-based semi-supervised learning. The essence of the EKI procedure is an ensemble based approximate gradient descent in which derivatives are replaced by differences from within the ensemble. We suggest several modifications to the basic method, derived from empirically successful heuristics developed in the context of SGD. Numerical results demonstrate wide applicability and robustness of the proposed algorithm.Comment: 41 pages, 14 figure

Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks

The standard probabilistic perspective on machine learning gives rise to empirical risk-minimization tasks that are frequently solved by stochastic gradient descent (SGD) and variants thereof. We present a formulation of these tasks as classical inverse or filtering problems and, furthermore, we propose an efficient, gradient-free algorithm for finding a solution to these problems using ensemble Kalman inversion (EKI). The method is inherently parallelizable and is applicable to problems with non-differentiable loss functions, for which back-propagation is not possible. Applications of our approach include offline and online supervised learning with deep neural networks, as well as graph-based semi-supervised learning. The essence of the EKI procedure is an ensemble based approximate gradient descent in which derivatives are replaced by differences from within the ensemble. We suggest several modifications to the basic method, derived from empirically successful heuristics developed in the context of SGD. Numerical results demonstrate wide applicability and robustness of the proposed algorithm

Image segmentation using superpixel ensembles

Recently there has been an increasing interest in image segmentation due to the needs of locating objects with high segmentation accuracy as required by many computer vision and image processing tasks. While image segmentation remains a research challenge, 'superpixel' as the perceptual meaningful grouping of pixels has become a popular concept and a number of superpixel-based image segmentation algorithms have been proposed. The goal of this thesis is to examine the state-of-the-art superpixel algorithms and introduce new methods for achieving better image segmentation outcome. To improve the accuracy of superpixel-based segmentation, we propose a colour covariance matrix-based segmentation algorithm (CCM). This algorithm employs a novel colour covariance descriptor and a corresponding similarity measure method. Moreover, based on the CCM algorithm, we propose a multi-layer bipartite graph model (MBG-CCM) and a low-rank representation technique based algorithm (LRR-CCM). In MBG-CCM, different superpixel descriptors are fused by a multi-layer bipartite graph, and in LRR-CCM, the similarities of the covariance descriptors of the superpixel are measured by the subspace structure. Besides, we develop a new over-segmentation, called superpixel association, and propose a novel segmentation algorithm (SHST) which is able to generate hierarchical segmentation from superpixel associations. In addition to those unsupervised segmentation algorithms, we also explore the algorithms for supervised segmentation. We propose a model for semantic segmentation, named 'generalized puzzle game', by which the segmentation information contained in the superpixels can be integrated into the supervised segmentation