411 research outputs found
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
This paper tackles the problem of large-scale image-based localization (IBL)
where the spatial location of a query image is determined by finding out the
most similar reference images in a large database. For solving this problem, a
critical task is to learn discriminative image representation that captures
informative information relevant for localization. We propose a novel
representation learning method having higher location-discriminating power. It
provides the following contributions: 1) we represent a place (location) as a
set of exemplar images depicting the same landmarks and aim to maximize
similarities among intra-place images while minimizing similarities among
inter-place images; 2) we model a similarity measure as a probability
distribution on L_2-metric distances between intra-place and inter-place image
representations; 3) we propose a new Stochastic Attraction and Repulsion
Embedding (SARE) loss function minimizing the KL divergence between the learned
and the actual probability distributions; 4) we give theoretical comparisons
between SARE, triplet ranking and contrastive losses. It provides insights into
why SARE is better by analyzing gradients. Our SARE loss is easy to implement
and pluggable to any CNN. Experiments show that our proposed method improves
the localization performance on standard benchmarks by a large margin.
Demonstrating the broad applicability of our method, we obtained the third
place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our
code and model are available at https://github.com/Liumouliu/deepIBL.Comment: ICC
Study on the mechanical behavior of directly compounded long glass fiber reinforced polyamide 6 composites
With great lightweight potential, high performance-to-cost ratio and mass productivity, direct-compounded long fiber thermoplastics (D-LFT) have drawn great attention from the automotive industry. With better mechanical properties and higher service temperature, polyamide 6 (PA6) was used to replace polypropylene (PP) which is almost the exclusively used matrix for the D-LFT process currently. The investigation was performed on this new material with a focus on the effect of fiber content, processing parameters, temperature and tailored reinforcement on mechanical behavior. The results show that the mechanical properties of this new material are sensitive to the variation of fiber content and service temperature but insensitive to the varied processing parameters. Tailored reinforcement technique is a feasible and predictable approach to adjust the mechanical properties of this new material
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion
RGB-guided depth completion aims at predicting dense depth maps from sparse
depth measurements and corresponding RGB images, where how to effectively and
efficiently exploit the multi-modal information is a key issue. Guided dynamic
filters, which generate spatially-variant depth-wise separable convolutional
filters from RGB features to guide depth features, have been proven to be
effective in this task. However, the dynamically generated filters require
massive model parameters, computational costs and memory footprints when the
number of feature channels is large. In this paper, we propose to decompose the
guided dynamic filters into a spatially-shared component multiplied by
content-adaptive adaptors at each spatial location. Based on the proposed idea,
we introduce two decomposition schemes A and B, which decompose the filters by
splitting the filter structure and using spatial-wise attention, respectively.
The decomposed filters not only maintain the favorable properties of guided
dynamic filters as being content-dependent and spatially-variant, but also
reduce model parameters and hardware costs, as the learned adaptors are
decoupled with the number of feature channels. Extensive experimental results
demonstrate that the methods using our schemes outperform state-of-the-art
methods on the KITTI dataset, and rank 1st and 2nd on the KITTI benchmark at
the time of submission. Meanwhile, they also achieve comparable performance on
the NYUv2 dataset. In addition, our proposed methods are general and could be
employed as plug-and-play feature fusion blocks in other multi-modal fusion
tasks such as RGB-D salient object detection
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