4,610 research outputs found
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
Unsupervised Object Discovery and Tracking in Video Collections
This paper addresses the problem of automatically localizing dominant objects
as spatio-temporal tubes in a noisy collection of videos with minimal or even
no supervision. We formulate the problem as a combination of two complementary
processes: discovery and tracking. The first one establishes correspondences
between prominent regions across videos, and the second one associates
successive similar object regions within the same video. Interestingly, our
algorithm also discovers the implicit topology of frames associated with
instances of the same object class across different videos, a role normally
left to supervisory information in the form of class labels in conventional
image and video understanding methods. Indeed, as demonstrated by our
experiments, our method can handle video collections featuring multiple object
classes, and substantially outperforms the state of the art in colocalization,
even though it tackles a broader problem with much less supervision
Webly Supervised Learning of Convolutional Networks
We present an approach to utilize large amounts of web data for learning
CNNs. Specifically inspired by curriculum learning, we present a two-step
approach for CNN training. First, we use easy images to train an initial visual
representation. We then use this initial CNN and adapt it to harder, more
realistic images by leveraging the structure of data and categories. We
demonstrate that our two-stage CNN outperforms a fine-tuned CNN trained on
ImageNet on Pascal VOC 2012. We also demonstrate the strength of webly
supervised learning by localizing objects in web images and training a R-CNN
style detector. It achieves the best performance on VOC 2007 where no VOC
training data is used. Finally, we show our approach is quite robust to noise
and performs comparably even when we use image search results from March 2013
(pre-CNN image search era)
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
This paper addresses unsupervised discovery and localization of dominant
objects from a noisy image collection with multiple object classes. The setting
of this problem is fully unsupervised, without even image-level annotations or
any assumption of a single dominant class. This is far more general than
typical colocalization, cosegmentation, or weakly-supervised localization
tasks. We tackle the discovery and localization problem using a part-based
region matching approach: We use off-the-shelf region proposals to form a set
of candidate bounding boxes for objects and object parts. These regions are
efficiently matched across images using a probabilistic Hough transform that
evaluates the confidence for each candidate correspondence considering both
appearance and spatial consistency. Dominant objects are discovered and
localized by comparing the scores of candidate regions and selecting those that
stand out over other regions containing them. Extensive experimental
evaluations on standard benchmarks demonstrate that the proposed approach
significantly outperforms the current state of the art in colocalization, and
achieves robust object discovery in challenging mixed-class datasets.Comment: CVPR 201
Objects that Sound
In this paper our objectives are, first, networks that can embed audio and
visual inputs into a common space that is suitable for cross-modal retrieval;
and second, a network that can localize the object that sounds in an image,
given the audio signal. We achieve both these objectives by training from
unlabelled video using only audio-visual correspondence (AVC) as the objective
function. This is a form of cross-modal self-supervision from video.
To this end, we design new network architectures that can be trained for
cross-modal retrieval and localizing the sound source in an image, by using the
AVC task. We make the following contributions: (i) show that audio and visual
embeddings can be learnt that enable both within-mode (e.g. audio-to-audio) and
between-mode retrieval; (ii) explore various architectures for the AVC task,
including those for the visual stream that ingest a single image, or multiple
images, or a single image and multi-frame optical flow; (iii) show that the
semantic object that sounds within an image can be localized (using only the
sound, no motion or flow information); and (iv) give a cautionary tale on how
to avoid undesirable shortcuts in the data preparation.Comment: Appears in: European Conference on Computer Vision (ECCV) 201
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