58,394 research outputs found
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
We introduce a new loss function for the weakly-supervised training of
semantic image segmentation models based on three guiding principles: to seed
with weak localization cues, to expand objects based on the information about
which classes can occur in an image, and to constrain the segmentations to
coincide with object boundaries. We show experimentally that training a deep
convolutional neural network using the proposed loss function leads to
substantially better segmentations than previous state-of-the-art methods on
the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the
working mechanism of our method by a detailed experimental study that
illustrates how the segmentation quality is affected by each term of the
proposed loss function as well as their combinations.Comment: ECCV 201
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
Unsupervised Learning of Semantic Audio Representations
Even in the absence of any explicit semantic annotation, vast collections of
audio recordings provide valuable information for learning the categorical
structure of sounds. We consider several class-agnostic semantic constraints
that apply to unlabeled nonspeech audio: (i) noise and translations in time do
not change the underlying sound category, (ii) a mixture of two sound events
inherits the categories of the constituents, and (iii) the categories of events
in close temporal proximity are likely to be the same or related. Without
labels to ground them, these constraints are incompatible with classification
loss functions. However, they may still be leveraged to identify geometric
inequalities needed for triplet loss-based training of convolutional neural
networks. The result is low-dimensional embeddings of the input spectrograms
that recover 41% and 84% of the performance of their fully-supervised
counterparts when applied to downstream query-by-example sound retrieval and
sound event classification tasks, respectively. Moreover, in
limited-supervision settings, our unsupervised embeddings double the
state-of-the-art classification performance.Comment: Submitted to ICASSP 201
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