538 research outputs found
Multi-turn Inference Matching Network for Natural Language Inference
Natural Language Inference (NLI) is a fundamental and challenging task in
Natural Language Processing (NLP). Most existing methods only apply one-pass
inference process on a mixed matching feature, which is a concatenation of
different matching features between a premise and a hypothesis. In this paper,
we propose a new model called Multi-turn Inference Matching Network (MIMN) to
perform multi-turn inference on different matching features. In each turn, the
model focuses on one particular matching feature instead of the mixed matching
feature. To enhance the interaction between different matching features, a
memory component is employed to store the history inference information. The
inference of each turn is performed on the current matching feature and the
memory. We conduct experiments on three different NLI datasets. The
experimental results show that our model outperforms or achieves the
state-of-the-art performance on all the three datasets
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
Frequency shift of light in Kerr spacetime
The frequency shift of light in the gravitational field generated by a
rotating body is investigated. We consider the scenario in which both the light
source and the observer are in motion. The frequency shift is calculated up to
the second-and-half-order post-Newtonian order via two different methods and
the same result is achieved. The higher-order effects of the gravitational
source's rotation on the frequency shift is obtained
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