12,320 research outputs found
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
We propose a convolutional network with hierarchical classifiers for
per-pixel semantic segmentation, which is able to be trained on multiple,
heterogeneous datasets and exploit their semantic hierarchy. Our network is the
first to be simultaneously trained on three different datasets from the
intelligent vehicles domain, i.e. Cityscapes, GTSDB and Mapillary Vistas, and
is able to handle different semantic level-of-detail, class imbalances, and
different annotation types, i.e. dense per-pixel and sparse bounding-box
labels. We assess our hierarchical approach, by comparing against flat,
non-hierarchical classifiers and we show improvements in mean pixel accuracy of
13.0% for Cityscapes classes and 2.4% for Vistas classes and 32.3% for GTSDB
classes. Our implementation achieves inference rates of 17 fps at a resolution
of 520x706 for 108 classes running on a GPU.Comment: IEEE Intelligent Vehicles 201
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
How can we reuse existing knowledge, in the form of available datasets, when
solving a new and apparently unrelated target task from a set of unlabeled
data? In this work we make a first contribution to answer this question in the
context of image classification. We frame this quest as an active learning
problem and use zero-shot classifiers to guide the learning process by linking
the new task to the existing classifiers. By revisiting the dual formulation of
adaptive SVM, we reveal two basic conditions to choose greedily only the most
relevant samples to be annotated. On this basis we propose an effective active
learning algorithm which learns the best possible target classification model
with minimum human labeling effort. Extensive experiments on two challenging
datasets show the value of our approach compared to the state-of-the-art active
learning methodologies, as well as its potential to reuse past datasets with
minimal effort for future tasks
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Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.
Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems
A growing number of applications, e.g. video surveillance and medical image
analysis, require training recognition systems from large amounts of weakly
annotated data while some targeted interactions with a domain expert are
allowed to improve the training process. In such cases, active learning (AL)
can reduce labeling costs for training a classifier by querying the expert to
provide the labels of most informative instances. This paper focuses on AL
methods for instance classification problems in multiple instance learning
(MIL), where data is arranged into sets, called bags, that are weakly labeled.
Most AL methods focus on single instance learning problems. These methods are
not suitable for MIL problems because they cannot account for the bag structure
of data. In this paper, new methods for bag-level aggregation of instance
informativeness are proposed for multiple instance active learning (MIAL). The
\textit{aggregated informativeness} method identifies the most informative
instances based on classifier uncertainty, and queries bags incorporating the
most information. The other proposed method, called \textit{cluster-based
aggregative sampling}, clusters data hierarchically in the instance space. The
informativeness of instances is assessed by considering bag labels, inferred
instance labels, and the proportion of labels that remain to be discovered in
clusters. Both proposed methods significantly outperform reference methods in
extensive experiments using benchmark data from several application domains.
Results indicate that using an appropriate strategy to address MIAL problems
yields a significant reduction in the number of queries needed to achieve the
same level of performance as single instance AL methods
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