17 research outputs found
Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.Comment: CVPR 201
Click Carving: Segmenting Objects in Video with Point Clicks
We present a novel form of interactive video object segmentation where a few
clicks by the user helps the system produce a full spatio-temporal segmentation
of the object of interest. Whereas conventional interactive pipelines take the
user's initialization as a starting point, we show the value in the system
taking the lead even in initialization. In particular, for a given video frame,
the system precomputes a ranked list of thousands of possible segmentation
hypotheses (also referred to as object region proposals) using image and motion
cues. Then, the user looks at the top ranked proposals, and clicks on the
object boundary to carve away erroneous ones. This process iterates (typically
2-3 times), and each time the system revises the top ranked proposal set, until
the user is satisfied with a resulting segmentation mask. Finally, the mask is
propagated across the video to produce a spatio-temporal object tube. On three
challenging datasets, we provide extensive comparisons with both existing work
and simpler alternative methods. In all, the proposed Click Carving approach
strikes an excellent balance of accuracy and human effort. It outperforms all
similarly fast methods, and is competitive or better than those requiring 2 to
12 times the effort.Comment: A preliminary version of the material in this document was filed as
University of Texas technical report no. UT AI16-0
Much Ado About Time: Exhaustive Annotation of Temporal Data
Large-scale annotated datasets allow AI systems to learn from and build upon
the knowledge of the crowd. Many crowdsourcing techniques have been developed
for collecting image annotations. These techniques often implicitly rely on the
fact that a new input image takes a negligible amount of time to perceive. In
contrast, we investigate and determine the most cost-effective way of obtaining
high-quality multi-label annotations for temporal data such as videos. Watching
even a short 30-second video clip requires a significant time investment from a
crowd worker; thus, requesting multiple annotations following a single viewing
is an important cost-saving strategy. But how many questions should we ask per
video? We conclude that the optimal strategy is to ask as many questions as
possible in a HIT (up to 52 binary questions after watching a 30-second video
clip in our experiments). We demonstrate that while workers may not correctly
answer all questions, the cost-benefit analysis nevertheless favors consensus
from multiple such cheap-yet-imperfect iterations over more complex
alternatives. When compared with a one-question-per-video baseline, our method
is able to achieve a 10% improvement in recall 76.7% ours versus 66.7%
baseline) at comparable precision (83.8% ours versus 83.0% baseline) in about
half the annotation time (3.8 minutes ours compared to 7.1 minutes baseline).
We demonstrate the effectiveness of our method by collecting multi-label
annotations of 157 human activities on 1,815 videos.Comment: HCOMP 2016 Camera Read
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Supervoxel-Consistent Foreground Propagation in Video
Abstract. A major challenge in video segmentation is that the fore-ground object may move quickly in the scene at the same time its ap-pearance and shape evolves over time. While pairwise potentials used in graph-based algorithms help smooth labels between neighboring (su-per)pixels in space and time, they offer only a myopic view of consis-tency and can be misled by inter-frame optical flow errors. We propose a higher order supervoxel label consistency potential for semi-supervised foreground segmentation. Given an initial frame with manual annota-tion for the foreground object, our approach propagates the foreground region through time, leveraging bottom-up supervoxels to guide its es-timates towards long-range coherent regions. We validate our approach on three challenging datasets and achieve state-of-the-art results.