74 research outputs found
Repulsion Loss: Detecting Pedestrians in a Crowd
Detecting individual pedestrians in a crowd remains a challenging problem
since the pedestrians often gather together and occlude each other in
real-world scenarios. In this paper, we first explore how a state-of-the-art
pedestrian detector is harmed by crowd occlusion via experimentation, providing
insights into the crowd occlusion problem. Then, we propose a novel bounding
box regression loss specifically designed for crowd scenes, termed repulsion
loss. This loss is driven by two motivations: the attraction by target, and the
repulsion by other surrounding objects. The repulsion term prevents the
proposal from shifting to surrounding objects thus leading to more crowd-robust
localization. Our detector trained by repulsion loss outperforms all the
state-of-the-art methods with a significant improvement in occlusion cases.Comment: Accepted to IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect
How to handle time features shall be the core question of any time series
forecasting model. Ironically, it is often ignored or misunderstood by
deep-learning based models, even those baselines which are state-of-the-art.
This behavior makes their inefficient, untenable and unstable. In this paper,
we rigorously analyze three prevalent but deficient/unfounded deep time series
forecasting mechanisms or methods from the view of time series properties,
including normalization methods, multivariate forecasting and input sequence
length. Corresponding corollaries and solutions are given on both empirical and
theoretical basis. We thereby propose a novel time series forecasting network,
i.e. RTNet, on the basis of aforementioned analysis. It is general enough to be
combined with both supervised and self-supervised forecasting format. Thanks to
the core idea of respecting time series properties, no matter in which
forecasting format, RTNet shows obviously superior forecasting performances
compared with dozens of other SOTA time series forecasting baselines in three
real-world benchmark datasets. By and large, it even occupies less time
complexity and memory usage while acquiring better forecasting accuracy. The
source code is available at https://github.com/OrigamiSL/RTNet
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
We present a novel end-to-end reinforcement learning approach to automatic
taxonomy induction from a set of terms. While prior methods treat the problem
as a two-phase task (i.e., detecting hypernymy pairs followed by organizing
these pairs into a tree-structured hierarchy), we argue that such two-phase
methods may suffer from error propagation, and cannot effectively optimize
metrics that capture the holistic structure of a taxonomy. In our approach, the
representations of term pairs are learned using multiple sources of information
and used to determine \textit{which} term to select and \textit{where} to place
it on the taxonomy via a policy network. All components are trained in an
end-to-end manner with cumulative rewards, measured by a holistic tree metric
over the training taxonomies. Experiments on two public datasets of different
domains show that our approach outperforms prior state-of-the-art taxonomy
induction methods up to 19.6\% on ancestor F1.Comment: 11 Pages. ACL 2018 Camera Read
- …