90 research outputs found
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving. However, to train CNNs requires a
considerable amount of data, which is difficult to collect and laborious to
annotate. Recent advances in computer graphics make it possible to train CNNs
on photo-realistic synthetic imagery with computer-generated annotations.
Despite this, the domain mismatch between the real images and the synthetic
data cripples the models' performance. Hence, we propose a curriculum-style
learning approach to minimize the domain gap in urban scenery semantic
segmentation. The curriculum domain adaptation solves easy tasks first to infer
necessary properties about the target domain; in particular, the first task is
to learn global label distributions over images and local distributions over
landmark superpixels. These are easy to estimate because images of urban scenes
have strong idiosyncrasies (e.g., the size and spatial relations of buildings,
streets, cars, etc.). We then train a segmentation network while regularizing
its predictions in the target domain to follow those inferred properties. In
experiments, our method outperforms the baselines on two datasets and two
backbone networks. We also report extensive ablation studies about our
approach.Comment: This is the extended version of the ICCV 2017 paper "Curriculum
Domain Adaptation for Semantic Segmentation of Urban Scenes" with additional
GTA experimen
Improved Dropout for Shallow and Deep Learning
Dropout has been witnessed with great success in training deep neural
networks by independently zeroing out the outputs of neurons at random. It has
also received a surge of interest for shallow learning, e.g., logistic
regression. However, the independent sampling for dropout could be suboptimal
for the sake of convergence. In this paper, we propose to use multinomial
sampling for dropout, i.e., sampling features or neurons according to a
multinomial distribution with different probabilities for different
features/neurons. To exhibit the optimal dropout probabilities, we analyze the
shallow learning with multinomial dropout and establish the risk bound for
stochastic optimization. By minimizing a sampling dependent factor in the risk
bound, we obtain a distribution-dependent dropout with sampling probabilities
dependent on the second order statistics of the data distribution. To tackle
the issue of evolving distribution of neurons in deep learning, we propose an
efficient adaptive dropout (named \textbf{evolutional dropout}) that computes
the sampling probabilities on-the-fly from a mini-batch of examples. Empirical
studies on several benchmark datasets demonstrate that the proposed dropouts
achieve not only much faster convergence and but also a smaller testing error
than the standard dropout. For example, on the CIFAR-100 data, the evolutional
dropout achieves relative improvements over 10\% on the prediction performance
and over 50\% on the convergence speed compared to the standard dropout.Comment: In NIPS 201
Improving Facial Attribute Prediction using Semantic Segmentation
Attributes are semantically meaningful characteristics whose applicability
widely crosses category boundaries. They are particularly important in
describing and recognizing concepts where no explicit training example is
given, \textit{e.g., zero-shot learning}. Additionally, since attributes are
human describable, they can be used for efficient human-computer interaction.
In this paper, we propose to employ semantic segmentation to improve facial
attribute prediction. The core idea lies in the fact that many facial
attributes describe local properties. In other words, the probability of an
attribute to appear in a face image is far from being uniform in the spatial
domain. We build our facial attribute prediction model jointly with a deep
semantic segmentation network. This harnesses the localization cues learned by
the semantic segmentation to guide the attention of the attribute prediction to
the regions where different attributes naturally show up. As a result of this
approach, in addition to recognition, we are able to localize the attributes,
despite merely having access to image level labels (weak supervision) during
training. We evaluate our proposed method on CelebA and LFWA datasets and
achieve superior results to the prior arts. Furthermore, we show that in the
reverse problem, semantic face parsing improves when facial attributes are
available. That reaffirms the need to jointly model these two interconnected
tasks
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
The recent success of deep neural networks is powered in part by large-scale
well-labeled training data. However, it is a daunting task to laboriously
annotate an ImageNet-like dateset. On the contrary, it is fairly convenient,
fast, and cheap to collect training images from the Web along with their noisy
labels. This signifies the need of alternative approaches to training deep
neural networks using such noisy labels. Existing methods tackling this problem
either try to identify and correct the wrong labels or reweigh the data terms
in the loss function according to the inferred noisy rates. Both strategies
inevitably incur errors for some of the data points. In this paper, we contend
that it is actually better to ignore the labels of some of the data points than
to keep them if the labels are incorrect, especially when the noisy rate is
high. After all, the wrong labels could mislead a neural network to a bad local
optimum. We suggest a two-stage framework for the learning from noisy labels.
In the first stage, we identify a small portion of images from the noisy
training set of which the labels are correct with a high probability. The noisy
labels of the other images are ignored. In the second stage, we train a deep
neural network in a semi-supervised manner. This framework effectively takes
advantage of the whole training set and yet only a portion of its labels that
are most likely correct. Experiments on three datasets verify the effectiveness
of our approach especially when the noisy rate is high
Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach
Recent years have witnessed a resurgence of interest in video summarization.
However, one of the main obstacles to the research on video summarization is
the user subjectivity - users have various preferences over the summaries. The
subjectiveness causes at least two problems. First, no single video summarizer
fits all users unless it interacts with and adapts to the individual users.
Second, it is very challenging to evaluate the performance of a video
summarizer.
To tackle the first problem, we explore the recently proposed query-focused
video summarization which introduces user preferences in the form of text
queries about the video into the summarization process. We propose a memory
network parameterized sequential determinantal point process in order to attend
the user query onto different video frames and shots. To address the second
challenge, we contend that a good evaluation metric for video summarization
should focus on the semantic information that humans can perceive rather than
the visual features or temporal overlaps. To this end, we collect dense
per-video-shot concept annotations, compile a new dataset, and suggest an
efficient evaluation method defined upon the concept annotations. We conduct
extensive experiments contrasting our video summarizer to existing ones and
present detailed analyses about the dataset and the new evaluation method
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