852 research outputs found
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation
learning, yet acquiring dense and pixel-wise ground-truth is both labor- and
time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an
economical alternative, with which training phase could hardly generate
satisfactory performance unfortunately. In order to generate high-quality
annotated data with a low time cost for accurate segmentation, in this paper,
we propose a novel annotation enrichment strategy, which expands existing
coarse annotations of training data to a finer scale. Extensive experiments on
the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural
networks trained with the enriched annotations from our framework yield a
significant improvement over that trained with the original coarse labels. It
is highly competitive to the performance obtained by using human annotated
dense annotations. The proposed method also outperforms among other
state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge
Managemen
Harvesting Information from Captions for Weakly Supervised Semantic Segmentation
Since acquiring pixel-wise annotations for training convolutional neural
networks for semantic image segmentation is time-consuming, weakly supervised
approaches that only require class tags have been proposed. In this work, we
propose another form of supervision, namely image captions as they can be found
on the Internet. These captions have two advantages. They do not require
additional curation as it is the case for the clean class tags used by current
weakly supervised approaches and they provide textual context for the classes
present in an image. To leverage such textual context, we deploy a multi-modal
network that learns a joint embedding of the visual representation of the image
and the textual representation of the caption. The network estimates text
activation maps (TAMs) for class names as well as compound concepts, i.e.
combinations of nouns and their attributes. The TAMs of compound concepts
describing classes of interest substantially improve the quality of the
estimated class activation maps which are then used to train a network for
semantic segmentation. We evaluate our method on the COCO dataset where it
achieves state of the art results for weakly supervised image segmentation
Budget-aware Semi-Supervised Semantic and Instance Segmentation
Methods that move towards less supervised scenarios are key for image
segmentation, as dense labels demand significant human intervention. Generally,
the annotation burden is mitigated by labeling datasets with weaker forms of
supervision, e.g. image-level labels or bounding boxes. Another option are
semi-supervised settings, that commonly leverage a few strong annotations and a
huge number of unlabeled/weakly-labeled data. In this paper, we revisit
semi-supervised segmentation schemes and narrow down significantly the
annotation budget (in terms of total labeling time of the training set)
compared to previous approaches. With a very simple pipeline, we demonstrate
that at low annotation budgets, semi-supervised methods outperform by a wide
margin weakly-supervised ones for both semantic and instance segmentation. Our
approach also outperforms previous semi-supervised works at a much reduced
labeling cost. We present results for the Pascal VOC benchmark and unify weakly
and semi-supervised approaches by considering the total annotation budget, thus
allowing a fairer comparison between methods.Comment: To appear in CVPR-W 2019 (DeepVision workshop
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
The main obstacle to weakly supervised semantic image segmentation is the
difficulty of obtaining pixel-level information from coarse image-level
annotations. Most methods based on image-level annotations use localization
maps obtained from the classifier, but these only focus on the small
discriminative parts of objects and do not capture precise boundaries.
FickleNet explores diverse combinations of locations on feature maps created by
generic deep neural networks. It selects hidden units randomly and then uses
them to obtain activation scores for image classification. FickleNet implicitly
learns the coherence of each location in the feature maps, resulting in a
localization map which identifies both discriminative and other parts of
objects. The ensemble effects are obtained from a single network by selecting
random hidden unit pairs, which means that a variety of localization maps are
generated from a single image. Our approach does not require any additional
training steps and only adds a simple layer to a standard convolutional neural
network; nevertheless it outperforms recent comparable techniques on the Pascal
VOC 2012 benchmark in both weakly and semi-supervised settings.Comment: To appear in CVPR 201
Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is receiving great attention due to
its low human annotation cost. In this paper, we aim to tackle bounding box
supervised semantic segmentation, i.e., training accurate semantic segmentation
models using bounding box annotations as supervision. To this end, we propose
Affinity Attention Graph Neural Network (GNN). Following previous
practices, we first generate pseudo semantic-aware seeds, which are then formed
into semantic graphs based on our newly proposed affinity Convolutional Neural
Network (CNN). Then the built graphs are input to our GNN, in which an
affinity attention layer is designed to acquire the short- and long- distance
information from soft graph edges to accurately propagate semantic labels from
the confident seeds to the unlabeled pixels. However, to guarantee the
precision of the seeds, we only adopt a limited number of confident pixel seed
labels for GNN, which may lead to insufficient supervision for training.
To alleviate this issue, we further introduce a new loss function and a
consistency-checking mechanism to leverage the bounding box constraint, so that
more reliable guidance can be included for the model optimization. Experiments
show that our approach achieves new state-of-the-art performances on Pascal VOC
2012 datasets (val: 76.5\%, test: 75.2\%). More importantly, our approach can
be readily applied to bounding box supervised instance segmentation task or
other weakly supervised semantic segmentation tasks, with state-of-the-art or
comparable performance among almot all weakly supervised tasks on PASCAL VOC or
COCO dataset. Our source code will be available at
https://github.com/zbf1991/A2GNN.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (TAPMI 2021
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