17,529 research outputs found
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer
vision. Majority of the efforts have been spent either to design the best
feature representation or to learn the optimal matching metric. Most approaches
have neglected the problem of adapting the selected features or the learned
model over time. To address such a problem, we propose a temporal model
adaptation scheme with human in the loop. We first introduce a
similarity-dissimilarity learning method which can be trained in an incremental
fashion by means of a stochastic alternating directions methods of multipliers
optimization procedure. Then, to achieve temporal adaptation with limited human
effort, we exploit a graph-based approach to present the user only the most
informative probe-gallery matches that should be used to update the model.
Results on three datasets have shown that our approach performs on par or even
better than state-of-the-art approaches while reducing the manual pairwise
labeling effort by about 80%
Semantic Image Segmentation via Deep Parsing Network
This paper addresses semantic image segmentation by incorporating rich
information into Markov Random Field (MRF), including high-order relations and
mixture of label contexts. Unlike previous works that optimized MRFs using
iterative algorithm, we solve MRF by proposing a Convolutional Neural Network
(CNN), namely Deep Parsing Network (DPN), which enables deterministic
end-to-end computation in a single forward pass. Specifically, DPN extends a
contemporary CNN architecture to model unary terms and additional layers are
carefully devised to approximate the mean field algorithm (MF) for pairwise
terms. It has several appealing properties. First, different from the recent
works that combined CNN and MRF, where many iterations of MF were required for
each training image during back-propagation, DPN is able to achieve high
performance by approximating one iteration of MF. Second, DPN represents
various types of pairwise terms, making many existing works as its special
cases. Third, DPN makes MF easier to be parallelized and speeded up in
Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC
2012 dataset, where a single DPN model yields a new state-of-the-art
segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201
- …