250 research outputs found
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Most progress in semantic segmentation reports on daytime images taken under
favorable illumination conditions. We instead address the problem of semantic
segmentation of nighttime images 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 via
labeled synthetic images and unlabeled real images, both for progressively
darker times of day, which exploits cross-time-of-day correspondences for the
real images to guide the inference of their labels; 2) a novel
uncertainty-aware annotation and evaluation framework and metric for semantic
segmentation, designed for adverse conditions and including image regions
beyond human recognition capability in the evaluation in a principled fashion;
3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920
unlabeled twilight images with correspondences to their daytime counterparts
plus a set of 151 nighttime images with fine pixel-level annotations created
with our protocol, which serves as a first benchmark to perform our novel
evaluation. Experiments show that our guided curriculum adaptation
significantly outperforms state-of-the-art methods on real nighttime sets both
for standard metrics and our uncertainty-aware metric. Furthermore, our
uncertainty-aware evaluation reveals that selective invalidation of predictions
can lead to better results on data with ambiguous content such as our nighttime
benchmark and profit safety-oriented applications which involve invalid inputs.Comment: ICCV 2019 camera-read
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
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
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
This work addresses the problem of semantic scene understanding under foggy
road conditions. Although marked progress has been made in semantic scene
understanding over the recent years, it is mainly concentrated on clear weather
outdoor scenes. Extending semantic segmentation methods to adverse weather
conditions like fog is crucially important for outdoor applications such as
self-driving cars. In this paper, we propose a novel method, which uses purely
synthetic data to improve the performance on unseen real-world foggy scenes
captured in the streets of Zurich and its surroundings. Our results highlight
the potential and power of photo-realistic synthetic images for training and
especially fine-tuning deep neural nets. Our contributions are threefold, 1) we
created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor
scenes, that we call Foggy Synscapes and plan to release publicly 2) we show
that with this data we outperform previous approaches on real-world foggy test
data 3) we show that a combination of our data and previously used data can
even further improve the performance on real-world foggy data.Comment: independent class IoU scores corrected for BiSiNet architectur
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
Due to the scarcity of dense pixel-level semantic annotations for images
recorded in adverse visual conditions, there has been a keen interest in
unsupervised domain adaptation (UDA) for the semantic segmentation of such
images. UDA adapts models trained on normal conditions to the target
adverse-condition domains. Meanwhile, multiple datasets with driving scenes
provide corresponding images of the same scenes across multiple conditions,
which can serve as a form of weak supervision for domain adaptation. We propose
Refign, a generic extension to self-training-based UDA methods which leverages
these cross-domain correspondences. Refign consists of two steps: (1) aligning
the normal-condition image to the corresponding adverse-condition image using
an uncertainty-aware dense matching network, and (2) refining the adverse
prediction with the normal prediction using an adaptive label correction
mechanism. We design custom modules to streamline both steps and set the new
state of the art for domain-adaptive semantic segmentation on several
adverse-condition benchmarks, including ACDC and Dark Zurich. The approach
introduces no extra training parameters, minimal computational overhead --
during training only -- and can be used as a drop-in extension to improve any
given self-training-based UDA method. Code is available at
https://github.com/brdav/refign.Comment: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
202
- …