10,705 research outputs found
The Lov\'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
The Jaccard index, also referred to as the intersection-over-union score, is
commonly employed in the evaluation of image segmentation results given its
perceptual qualities, scale invariance - which lends appropriate relevance to
small objects, and appropriate counting of false negatives, in comparison to
per-pixel losses. We present a method for direct optimization of the mean
intersection-over-union loss in neural networks, in the context of semantic
image segmentation, based on the convex Lov\'asz extension of submodular
losses. The loss is shown to perform better with respect to the Jaccard index
measure than the traditionally used cross-entropy loss. We show quantitative
and qualitative differences between optimizing the Jaccard index per image
versus optimizing the Jaccard index taken over an entire dataset. We evaluate
the impact of our method in a semantic segmentation pipeline and show
substantially improved intersection-over-union segmentation scores on the
Pascal VOC and Cityscapes datasets using state-of-the-art deep learning
segmentation architectures.Comment: Accepted as a conference paper at CVPR 201
Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.
Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
Current CNN-based algorithms for recovering the 3D pose of an object in an
image assume knowledge about both the object category and its 2D localization
in the image. In this paper, we relax one of these constraints and propose to
solve the task of joint object category and 3D pose estimation from an image
assuming known 2D localization. We design a new architecture for this task
composed of a feature network that is shared between subtasks, an object
categorization network built on top of the feature network, and a collection of
category dependent pose regression networks. We also introduce suitable loss
functions and a training method for the new architecture. Experiments on the
challenging PASCAL3D+ dataset show state-of-the-art performance in the joint
categorization and pose estimation task. Moreover, our performance on the joint
task is comparable to the performance of state-of-the-art methods on the
simpler 3D pose estimation with known object category task
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