6,020 research outputs found
Detection is the central problem in real-word spelling correction
Real-word spelling correction differs from non-word spelling correction in
its aims and its challenges. Here we show that the central problem in real-word
spelling correction is detection. Methods from non-word spelling correction,
which focus instead on selection among candidate corrections, do not address
detection adequately, because detection is either assumed in advance or heavily
constrained. As we demonstrate in this paper, merely discriminating between the
intended word and a random close variation of it within the context of a
sentence is a task that can be performed with high accuracy using
straightforward models. Trigram models are sufficient in almost all cases. The
difficulty comes when every word in the sentence is a potential error, with a
large set of possible candidate corrections. Despite their strengths, trigram
models cannot reliably find true errors without introducing many more, at least
not when used in the obvious sequential way without added structure. The
detection task exposes weakness not visible in the selection task
Learning to Refine Human Pose Estimation
Multi-person pose estimation in images and videos is an important yet
challenging task with many applications. Despite the large improvements in
human pose estimation enabled by the development of convolutional neural
networks, there still exist a lot of difficult cases where even the
state-of-the-art models fail to correctly localize all body joints. This
motivates the need for an additional refinement step that addresses these
challenging cases and can be easily applied on top of any existing method. In
this work, we introduce a pose refinement network (PoseRefiner) which takes as
input both the image and a given pose estimate and learns to directly predict a
refined pose by jointly reasoning about the input-output space. In order for
the network to learn to refine incorrect body joint predictions, we employ a
novel data augmentation scheme for training, where we model "hard" human pose
cases. We evaluate our approach on four popular large-scale pose estimation
benchmarks such as MPII Single- and Multi-Person Pose Estimation, PoseTrack
Pose Estimation, and PoseTrack Pose Tracking, and report systematic improvement
over the state of the art.Comment: To appear in CVPRW (2018). Workshop: Visual Understanding of Humans
in Crowd Scene and the 2nd Look Into Person Challenge (VUHCS-LIP
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