1,175 research outputs found
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
This work addresses the problem of semantic image segmentation of nighttime
scenes. Although considerable progress has been made in semantic image
segmentation, it is mainly related to daytime scenarios. This paper proposes a
novel method to progressive adapt the semantic models trained on daytime
scenes, along with large-scale annotations therein, to nighttime scenes via the
bridge of twilight time -- the time between dawn and sunrise, or between sunset
and dusk. The goal of the method is to alleviate the cost of human annotation
for nighttime images by transferring knowledge from standard daytime
conditions. In addition to the method, a new dataset of road scenes is
compiled; it consists of 35,000 images ranging from daytime to twilight time
and to nighttime. Also, a subset of the nighttime images are densely annotated
for method evaluation. Our experiments show that our method is effective for
model adaptation from daytime scenes to nighttime scenes, without using extra
human annotation.Comment: Accepted to International Conference on Intelligent Transportation
Systems (ITSC 2018
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
We address the problem of semantic nighttime image segmentation and improve
the state-of-the-art, by adapting daytime models to nighttime without using
nighttime annotations. Moreover, we design a new evaluation framework to
address the substantial uncertainty of semantics in nighttime images. Our
central contributions are: 1) a curriculum framework to gradually adapt
semantic segmentation models from day to night through progressively darker
times of day, exploiting cross-time-of-day correspondences between daytime
images from a reference map and dark images to guide the label inference in the
dark domains; 2) a novel uncertainty-aware annotation and evaluation framework
and metric for semantic segmentation, including image regions beyond human
recognition capability in the evaluation in a principled fashion; 3) the Dark
Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight
images with correspondences to their daytime counterparts plus a set of 201
nighttime images with fine pixel-level annotations created with our protocol,
which serves as a first benchmark for our novel evaluation. Experiments show
that our map-guided curriculum adaptation significantly outperforms
state-of-the-art methods on nighttime sets both for standard metrics and our
uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals
that selective invalidation of predictions can improve results on data with
ambiguous content such as our benchmark and profit safety-oriented applications
involving invalid inputs.Comment: IEEE T-PAMI 202
Semi-Supervised Learning by Augmented Distribution Alignment
In this work, we propose a simple yet effective semi-supervised learning
approach called Augmented Distribution Alignment. We reveal that an essential
sampling bias exists in semi-supervised learning due to the limited number of
labeled samples, which often leads to a considerable empirical distribution
mismatch between labeled data and unlabeled data. To this end, we propose to
align the empirical distributions of labeled and unlabeled data to alleviate
the bias. On one hand, we adopt an adversarial training strategy to minimize
the distribution distance between labeled and unlabeled data as inspired by
domain adaptation works. On the other hand, to deal with the small sample size
issue of labeled data, we also propose a simple interpolation strategy to
generate pseudo training samples. Those two strategies can be easily
implemented into existing deep neural networks. We demonstrate the
effectiveness of our proposed approach on the benchmark SVHN and CIFAR10
datasets. Our code is available at \url{https://github.com/qinenergy/adanet}.Comment: To appear in ICCV 201
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
Generative modeling over natural images is one of the most fundamental
machine learning problems. However, few modern generative models, including
Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued
images that are frequently encountered in real-world applications. To fill the
gap, this paper first formulates the problem of generating manifold-valued
images and exploits three typical instances: hue-saturation-value (HSV) color
image generation, chromaticity-brightness (CB) color image generation, and
diffusion-tensor (DT) image generation. For the proposed generative modeling
problem, we then introduce a theorem of optimal transport to derive a new
Wasserstein distance of data distributions on complete manifolds, enabling us
to achieve a tractable objective under the WGAN framework. In addition, we
recommend three benchmark datasets that are CIFAR-10 HSV/CB color images,
ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we
experimentally demonstrate the proposed manifold-aware WGAN model can generate
more plausible manifold-valued images than its competitors.Comment: Accepted by AAAI 201
Building Deep Networks on Grassmann Manifolds
Learning representations on Grassmann manifolds is popular in quite a few
visual recognition tasks. In order to enable deep learning on Grassmann
manifolds, this paper proposes a deep network architecture by generalizing the
Euclidean network paradigm to Grassmann manifolds. In particular, we design
full rank mapping layers to transform input Grassmannian data to more desirable
ones, exploit re-orthonormalization layers to normalize the resulting matrices,
study projection pooling layers to reduce the model complexity in the
Grassmannian context, and devise projection mapping layers to respect
Grassmannian geometry and meanwhile achieve Euclidean forms for regular output
layers. To train the Grassmann networks, we exploit a stochastic gradient
descent setting on manifolds of the connection weights, and study a matrix
generalization of backpropagation to update the structured data. The
evaluations on three visual recognition tasks show that our Grassmann networks
have clear advantages over existing Grassmann learning methods, and achieve
results comparable with state-of-the-art approaches.Comment: AAAI'18 pape
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