127,517 research outputs found
A novel active learning technique for multi-label remote sensing image scene classification
Copyright 2018 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.This paper presents a novel multi-label active learning (MLAL) technique in the framework of multi-label remote sensing (RS) image scene classification problems. The proposed MLAL technique is developed in the framework of the multi-label SVM classifier (ML-SVM). Unlike the standard AL methods, the proposed MLAL technique redefines active learning by evaluating the informativeness of each image based on its multiple land-cover classes. Accordingly, the proposed MLAL technique is based on the joint evaluation of two criteria for the selection of the most informative images: i) multi-label uncertainty and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the multi-label classification algorithm in correctly assigning multi-labels to each image, whereas multi-label diversity criterion aims at selecting a set of un-annotated images that are as more diverse as possible to reduce the redundancy among them. In order to evaluate the multi-label uncertainty of each image, we propose a novel multi-label margin sampling strategy that: 1) considers the functional distances of each image to all ML-SVM hyperplanes; and then 2) estimates the occurrence on how many times each image falls inside the margins of ML-SVMs. If the occurrence is small, the classifiers are confident to correctly classify the considered image, and vice versa. In order to evaluate the multi-label diversity of each image, we propose a novel clustering-based strategy that clusters all the images inside the margins of the ML-SVMs and avoids selecting the uncertain images from the same clusters. The joint use of the two criteria allows one to enrich the training set of images with multi-labels. Experimental results obtained on a benchmark archive with 2100 images with their multi-labels show the effectiveness of the proposed MLAL method compared to the standard AL methods that neglect the evaluation of the uncertainty and diversity on multi-labels.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart
Deep Active Learning Explored Across Diverse Label Spaces
abstract: Deep learning architectures have been widely explored in computer vision and have
depicted commendable performance in a variety of applications. A fundamental challenge
in training deep networks is the requirement of large amounts of labeled training
data. While gathering large quantities of unlabeled data is cheap and easy, annotating
the data is an expensive process in terms of time, labor and human expertise.
Thus, developing algorithms that minimize the human effort in training deep models
is of immense practical importance. Active learning algorithms automatically identify
salient and exemplar samples from large amounts of unlabeled data and can augment
maximal information to supervised learning models, thereby reducing the human annotation
effort in training machine learning models. The goal of this dissertation is to
fuse ideas from deep learning and active learning and design novel deep active learning
algorithms. The proposed learning methodologies explore diverse label spaces to
solve different computer vision applications. Three major contributions have emerged
from this work; (i) a deep active framework for multi-class image classication, (ii)
a deep active model with and without label correlation for multi-label image classi-
cation and (iii) a deep active paradigm for regression. Extensive empirical studies
on a variety of multi-class, multi-label and regression vision datasets corroborate the
potential of the proposed methods for real-world applications. Additional contributions
include: (i) a multimodal emotion database consisting of recordings of facial
expressions, body gestures, vocal expressions and physiological signals of actors enacting
various emotions, (ii) four multimodal deep belief network models and (iii)
an in-depth analysis of the effect of transfer of multimodal emotion features between
source and target networks on classification accuracy and training time. These related
contributions help comprehend the challenges involved in training deep learning
models and motivate the main goal of this dissertation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Deep Active Learning for Multi-Label Classification of Remote Sensing Images
In this letter, we introduce deep active learning (AL) for multi-label
classification (MLC) problems in remote sensing (RS). In particular, we
investigate the effectiveness of several AL query functions for MLC of RS
images. Unlike the existing AL query functions (which are defined for
single-label classification or semantic segmentation problems), each query
function in this paper is based on the evaluation of two criteria: i)
multi-label uncertainty; and ii) multi-label diversity. The multi-label
uncertainty criterion is associated to the confidence of the deep neural
networks (DNNs) in correctly assigning multi-labels to each image. To assess
this criterion, we investigate three strategies: i) learning multi-label loss
ordering; ii) measuring temporal discrepancy of multi-label predictions; and
iii) measuring magnitude of approximated gradient embeddings. The multi-label
diversity criterion is associated to the selection of a set of images that are
as diverse as possible to each other that prevents redundancy among them. To
assess this criterion, we exploit a clustering based strategy. We combine each
of the above-mentioned uncertainty strategies with the clustering based
diversity strategy, resulting in three different query functions. All the
considered query functions are introduced for the first time in the framework
of MLC problems in RS. Experimental results obtained on two benchmark archives
show that these query functions result in the selection of a highly informative
set of samples at each iteration of the AL process.Comment: Accepted to IEEE Geoscience and Remote Sensing Letter
One-bit Supervision for Image Classification: Problem, Solution, and Beyond
This paper presents one-bit supervision, a novel setting of learning with
fewer labels, for image classification. Instead of training model using the
accurate label of each sample, our setting requires the model to interact with
the system by predicting the class label of each sample and learn from the
answer whether the guess is correct, which provides one bit (yes or no) of
information. An intriguing property of the setting is that the burden of
annotation largely alleviates in comparison to offering the accurate label.
There are two keys to one-bit supervision, which are (i) improving the guess
accuracy and (ii) making good use of the incorrect guesses. To achieve these
goals, we propose a multi-stage training paradigm and incorporate negative
label suppression into an off-the-shelf semi-supervised learning algorithm.
Theoretical analysis shows that one-bit annotation is more efficient than
full-bit annotation in most cases and gives the conditions of combining our
approach with active learning. Inspired by this, we further integrate the
one-bit supervision framework into the self-supervised learning algorithm which
yields an even more efficient training schedule. Different from training from
scratch, when self-supervised learning is used for initialization, both hard
example mining and class balance are verified effective in boosting the
learning performance. However, these two frameworks still need full-bit labels
in the initial stage. To cast off this burden, we utilize unsupervised domain
adaptation to train the initial model and conduct pure one-bit annotations on
the target dataset. In multiple benchmarks, the learning efficiency of the
proposed approach surpasses that using full-bit, semi-supervised supervision.Comment: ACM TOMM. arXiv admin note: text overlap with arXiv:2009.0616
Minimizing Supervision in Multi-label Categorization
Multiple categories of objects are present in most images. Treating this as a
multi-class classification is not justified. We treat this as a multi-label
classification problem. In this paper, we further aim to minimize the
supervision required for providing supervision in multi-label classification.
Specifically, we investigate an effective class of approaches that associate a
weak localization with each category either in terms of the bounding box or
segmentation mask. Doing so improves the accuracy of multi-label
categorization. The approach we adopt is one of active learning, i.e.,
incrementally selecting a set of samples that need supervision based on the
current model, obtaining supervision for these samples, retraining the model
with the additional set of supervised samples and proceeding again to select
the next set of samples. A crucial concern is the choice of the set of samples.
In doing so, we provide a novel insight, and no specific measure succeeds in
obtaining a consistently improved selection criterion. We, therefore, provide a
selection criterion that consistently improves the overall baseline criterion
by choosing the top k set of samples for a varied set of criteria. Using this
criterion, we are able to show that we can retain more than 98% of the fully
supervised performance with just 20% of samples (and more than 96% using 10%)
of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach
consistently outperforms all other baseline metrics for all benchmark datasets
and model combinations.Comment: Accepted in CVPR-W 202
- …