16 research outputs found
Associative embeddings for large-scale knowledge transfer with self-assessment
We propose a method for knowledge transfer between semantically related
classes in ImageNet. By transferring knowledge from the images that have
bounding-box annotations to the others, our method is capable of automatically
populating ImageNet with many more bounding-boxes and even pixel-level
segmentations. The underlying assumption that objects from semantically related
classes look alike is formalized in our novel Associative Embedding (AE)
representation. AE recovers the latent low-dimensional space of appearance
variations among image windows. The dimensions of AE space tend to correspond
to aspects of window appearance (e.g. side view, close up, background). We
model the overlap of a window with an object using Gaussian Processes (GP)
regression, which spreads annotation smoothly through AE space. The
probabilistic nature of GP allows our method to perform self-assessment, i.e.
assigning a quality estimate to its own output. It enables trading off the
amount of returned annotations for their quality. A large scale experiment on
219 classes and 0.5 million images demonstrates that our method outperforms
state-of-the-art methods and baselines for both object localization and
segmentation. Using self-assessment we can automatically return bounding-box
annotations for 30% of all images with high localization accuracy (i.e.~73%
average overlap with ground-truth).Comment: A final CVPR version with a correction in (1). IEEE Computer Vision
and Pattern Recognition, 201
Object localization in ImageNet by looking out of the window
We propose a method for annotating the location of objects in ImageNet.
Traditionally, this is cast as an image window classification problem, where
each window is considered independently and scored based on its appearance
alone. Instead, we propose a method which scores each candidate window in the
context of all other windows in the image, taking into account their similarity
in appearance space as well as their spatial relations in the image plane. We
devise a fast and exact procedure to optimize our scoring function over all
candidate windows in an image, and we learn its parameters using structured
output regression. We demonstrate on 92000 images from ImageNet that this
significantly improves localization over recent techniques that score windows
in isolation.Comment: in BMVC 201
Joint calibration of Ensemble of Exemplar SVMs
We present a method for calibrating the Ensemble of Exemplar SVMs model.
Unlike the standard approach, which calibrates each SVM independently, our
method optimizes their joint performance as an ensemble. We formulate joint
calibration as a constrained optimization problem and devise an efficient
optimization algorithm to find its global optimum. The algorithm dynamically
discards parts of the solution space that cannot contain the optimum early on,
making the optimization computationally feasible. We experiment with EE-SVM
trained on state-of-the-art CNN descriptors. Results on the ILSVRC 2014 and
PASCAL VOC 2007 datasets show that (i) our joint calibration procedure
outperforms independent calibration on the task of classifying windows as
belonging to an object class or not; and (ii) this improved window classifier
leads to better performance on the object detection task
An active search strategy for efficient object class detection
Object class detectors typically apply a window classifier to all the windows
in a large set, either in a sliding window manner or using object proposals. In
this paper, we develop an active search strategy that sequentially chooses the
next window to evaluate based on all the information gathered before. This
results in a substantial reduction in the number of classifier evaluations and
in a more elegant approach in general. Our search strategy is guided by two
forces. First, we exploit context as the statistical relation between the
appearance of a window and its location relative to the object, as observed in
the training set. This enables to jump across distant regions in the image
(e.g. observing a sky region suggests that cars might be far below) and is done
efficiently in a Random Forest framework. Second, we exploit the score of the
classifier to attract the search to promising areas surrounding a highly scored
window, and to keep away from areas near low scored ones. Our search strategy
can be applied on top of any classifier as it treats it as a black-box. In
experiments with R-CNN on the challenging SUN2012 dataset, our method matches
the detection accuracy of evaluating all windows independently, while
evaluating 9x fewer windows