6,093 research outputs found
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
We present a semi-supervised approach that localizes multiple unknown object
instances in long videos. We start with a handful of labeled boxes and
iteratively learn and label hundreds of thousands of object instances. We
propose criteria for reliable object detection and tracking for constraining
the semi-supervised learning process and minimizing semantic drift. Our
approach does not assume exhaustive labeling of each object instance in any
single frame, or any explicit annotation of negative data. Working in such a
generic setting allow us to tackle multiple object instances in video, many of
which are static. In contrast, existing approaches either do not consider
multiple object instances per video, or rely heavily on the motion of the
objects present. The experiments demonstrate the effectiveness of our approach
by evaluating the automatically labeled data on a variety of metrics like
quality, coverage (recall), diversity, and relevance to training an object
detector.Comment: To appear in CVPR 201
Weakly supervised segment annotation via expectation kernel density estimation
Since the labelling for the positive images/videos is ambiguous in weakly
supervised segment annotation, negative mining based methods that only use the
intra-class information emerge. In these methods, negative instances are
utilized to penalize unknown instances to rank their likelihood of being an
object, which can be considered as a voting in terms of similarity. However,
these methods 1) ignore the information contained in positive bags, 2) only
rank the likelihood but cannot generate an explicit decision function. In this
paper, we propose a voting scheme involving not only the definite negative
instances but also the ambiguous positive instances to make use of the extra
useful information in the weakly labelled positive bags. In the scheme, each
instance votes for its label with a magnitude arising from the similarity, and
the ambiguous positive instances are assigned soft labels that are iteratively
updated during the voting. It overcomes the limitations of voting using only
the negative bags. We also propose an expectation kernel density estimation
(eKDE) algorithm to gain further insight into the voting mechanism.
Experimental results demonstrate the superiority of our scheme beyond the
baselines.Comment: 9 pages, 2 figure
Exploring Object Relation in Mean Teacher for Cross-Domain Detection
Rendering synthetic data (e.g., 3D CAD-rendered images) to generate
annotations for learning deep models in vision tasks has attracted increasing
attention in recent years. However, simply applying the models learnt on
synthetic images may lead to high generalization error on real images due to
domain shift. To address this issue, recent progress in cross-domain
recognition has featured the Mean Teacher, which directly simulates
unsupervised domain adaptation as semi-supervised learning. The domain gap is
thus naturally bridged with consistency regularization in a teacher-student
scheme. In this work, we advance this Mean Teacher paradigm to be applicable
for cross-domain detection. Specifically, we present Mean Teacher with Object
Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster
R-CNN by integrating the object relations into the measure of consistency cost
between teacher and student modules. Technically, MTOR firstly learns
relational graphs that capture similarities between pairs of regions for
teacher and student respectively. The whole architecture is then optimized with
three consistency regularizations: 1) region-level consistency to align the
region-level predictions between teacher and student, 2) inter-graph
consistency for matching the graph structures between teacher and student, and
3) intra-graph consistency to enhance the similarity between regions of same
class within the graph of student. Extensive experiments are conducted on the
transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results
are reported when comparing to state-of-the-art approaches. More remarkably, we
obtain a new record of single model: 22.8% of mAP on Syn2Real detection
dataset.Comment: CVPR 2019; The codes and model of our MTOR are publicly available at:
https://github.com/caiqi/mean-teacher-cross-domain-detectio
Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm
Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.Comment: CVPR 201
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