4,891 research outputs found
Weakly Supervised Object Localization Using Size Estimates
We present a technique for weakly supervised object localization (WSOL),
building on the observation that WSOL algorithms usually work better on images
with bigger objects. Instead of training the object detector on the entire
training set at the same time, we propose a curriculum learning strategy to
feed training images into the WSOL learning loop in an order from images
containing bigger objects down to smaller ones. To automatically determine the
order, we train a regressor to estimate the size of the object given the whole
image as input. Furthermore, we use these size estimates to further improve the
re-localization step of WSOL by assigning weights to object proposals according
to how close their size matches the estimated object size. We demonstrate the
effectiveness of using size order and size weighting on the challenging PASCAL
VOC 2007 dataset, where we achieve a significant improvement over existing
state-of-the-art WSOL techniques.Comment: ECCV 2016 camera-read
Weakly-Supervised Temporal Localization via Occurrence Count Learning
We propose a novel model for temporal detection and localization which allows
the training of deep neural networks using only counts of event occurrences as
training labels. This powerful weakly-supervised framework alleviates the
burden of the imprecise and time-consuming process of annotating event
locations in temporal data. Unlike existing methods, in which localization is
explicitly achieved by design, our model learns localization implicitly as a
byproduct of learning to count instances. This unique feature is a direct
consequence of the model's theoretical properties. We validate the
effectiveness of our approach in a number of experiments (drum hit and piano
onset detection in audio, digit detection in images) and demonstrate
performance comparable to that of fully-supervised state-of-the-art methods,
despite much weaker training requirements.Comment: Accepted at ICML 201
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
When supervising an object detector with weakly labeled data, most existing
approaches are prone to trapping in the discriminative object parts, e.g.,
finding the face of a cat instead of the full body, due to lacking the
supervision on the extent of full objects. To address this challenge, we
incorporate object segmentation into the detector training, which guides the
model to correctly localize the full objects. We propose the multiple instance
curriculum learning (MICL) method, which injects curriculum learning (CL) into
the multiple instance learning (MIL) framework. The MICL method starts by
automatically picking the easy training examples, where the extent of the
segmentation masks agree with detection bounding boxes. The training set is
gradually expanded to include harder examples to train strong detectors that
handle complex images. The proposed MICL method with segmentation in the loop
outperforms the state-of-the-art weakly supervised object detectors by a
substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201
Weakly Supervised Localization using Deep Feature Maps
Object localization is an important computer vision problem with a variety of
applications. The lack of large scale object-level annotations and the relative
abundance of image-level labels makes a compelling case for weak supervision in
the object localization task. Deep Convolutional Neural Networks are a class of
state-of-the-art methods for the related problem of object recognition. In this
paper, we describe a novel object localization algorithm which uses
classification networks trained on only image labels. This weakly supervised
method leverages local spatial and semantic patterns captured in the
convolutional layers of classification networks. We propose an efficient beam
search based approach to detect and localize multiple objects in images. The
proposed method significantly outperforms the state-of-the-art in standard
object localization data-sets with a 8 point increase in mAP scores
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
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