90 research outputs found
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.Comment: This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 201
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
In recent years, computer vision has made remarkable advancements in
autonomous driving and robotics. However, it has been observed that deep
learning-based visual perception models lack robustness when faced with camera
motion perturbations. The current certification process for assessing
robustness is costly and time-consuming due to the extensive number of image
projections required for Monte Carlo sampling in the 3D camera motion space. To
address these challenges, we present a novel, efficient, and practical
framework for certifying the robustness of 3D-2D projective transformations
against camera motion perturbations. Our approach leverages a smoothing
distribution over the 2D pixel space instead of in the 3D physical space,
eliminating the need for costly camera motion sampling and significantly
enhancing the efficiency of robustness certifications. With the pixel-wise
smoothed classifier, we are able to fully upper bound the projection errors
using a technique of uniform partitioning in camera motion space. Additionally,
we extend our certification framework to a more general scenario where only a
single-frame point cloud is required in the projection oracle. This is achieved
by deriving Lipschitz-based approximated partition intervals. Through extensive
experimentation, we validate the trade-off between effectiveness and efficiency
enabled by our proposed method. Remarkably, our approach achieves approximately
80% certified accuracy while utilizing only 30% of the projected image frames.Comment: 32 pages, 5 figures, 13 table
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
The past few years have witnessed an increasing interest in improving the
perception performance of LiDARs on autonomous vehicles. While most of the
existing works focus on developing new deep learning algorithms or model
architectures, we study the problem from the physical design perspective, i.e.,
how different placements of multiple LiDARs influence the learning-based
perception. To this end, we introduce an easy-to-compute information-theoretic
surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D
detection of different types of objects. We also present a new data collection,
detection model training and evaluation framework in the realistic CARLA
simulator to evaluate disparate multi-LiDAR configurations. Using several
prevalent placements inspired by the designs of self-driving companies, we show
the correlation between our surrogate metric and object detection performance
of different representative algorithms on KITTI through extensive experiments,
validating the effectiveness of our LiDAR placement evaluation approach. Our
results show that sensor placement is non-negligible in 3D point cloud-based
object detection, which will contribute up to 10% performance discrepancy in
terms of average precision in challenging 3D object detection settings. We
believe that this is one of the first studies to quantitatively investigate the
influence of LiDAR placement on perception performance. The code is available
on https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection.Comment: CVPR 2022 camera-ready version:15 pages, 14 figures, 9 table
Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments
Visual localization is a crucial problem in mobile robotics and autonomous
driving. One solution is to retrieve images with known pose from a database for
the localization of query images. However, in environments with drastically
varying conditions (e.g. illumination changes, seasons, occlusion, dynamic
objects), retrieval-based localization is severely hampered and becomes a
challenging problem. In this paper, a novel domain-invariant feature learning
method (DIFL) is proposed based on ComboGAN, a multi-domain image translation
network architecture. By introducing a feature consistency loss (FCL) between
the encoded features of the original image and translated image in another
domain, we are able to train the encoders to generate domain-invariant features
in a self-supervised manner. To retrieve a target image from the database, the
query image is first encoded using the encoder belonging to the query domain to
obtain a domain-invariant feature vector. We then preform retrieval by
selecting the database image with the most similar domain-invariant feature
vector. We validate the proposed approach on the CMU-Seasons dataset, where we
outperform state-of-the-art learning-based descriptors in retrieval-based
localization for high and medium precision scenarios.Comment: Accepted by 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019
Optimization of “Deoxidation Alloying” Batching Scheme
In this paper, a mathematical model was established to predict the deoxidation alloying and to optimize the type and quantity of input alloys. Firstly, the GCA method was used to obtain the main factors affecting the alloy yield of carbon and manganese based on the historical data. Secondly, the alloy yield was predicted by the stepwise MRA, the BP neural network and the regression SVM models, respectively. The conclusion is that the regression SVM model has the highest prediction accuracy and the maximum deviation between the test set prediction result and the real value was only 0.0682 and 0.0554. Thirdly, in order to reduce the manufacturer's production cost, the genetic algorithm was used to calculate the production cost mathematical programming model. Finally, sensitivity analysis was performed on the prediction model and the cost optimization model. The unit price of 20% of the alloy raw materials was increased by 20%, and the total cost change rate was 0.7155%, the lowest was -0.4297%, which proved that the mathematical model established presented strong robustness and could be certain reference value for the current production of iron and steel enterprises
VCP/p97, Down-Regulated by microRNA-129-5p, Could Regulate the Progression of Hepatocellular Carcinoma
Valosin containing protein (VCP)/p97 plays various important roles in cells. Moreover, elevated expression of VCP in hepatocellular carcinoma (HCC) is correlated with increased incidence of recurrence. But the role of VCP in HCC progression in vitro and in vivo is unclear. And there are few reports about the regulation mechanism on the expression of VCP in HCC. In this study, it was identified that the level of VCP was frequently increased in human HCC tissues. In addition, down-regulation of VCP with siRNAs could dramatically suppress the genesis and progression of tumor in vivo. It was found that miR-129-5p directly inhibited the expression of VCP in several HCC cell lines. Meanwhile, the level of VCP in HCC tissues was negatively associated with the level of miR-129-5p. Our further investigation showed that the enhanced expression of miR-129-5p also suppressed tumor growth in vivo. Moreover, it was revealed that miR-129-5p could inhibit the degradation of IκBα and increase the apoptosis and reduce the migration of HCC cells by suppressing the expression of VCP. Our results revealed that the expression of VCP was directly regulated by miR-129-5p and this regulation played an important role in the progression of HCC
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles
As shown by recent studies, machine intelligence-enabled systems are
vulnerable to test cases resulting from either adversarial manipulation or
natural distribution shifts. This has raised great concerns about deploying
machine learning algorithms for real-world applications, especially in the
safety-critical domains such as autonomous driving (AD). On the other hand,
traditional AD testing on naturalistic scenarios requires hundreds of millions
of driving miles due to the high dimensionality and rareness of the
safety-critical scenarios in the real world. As a result, several approaches
for autonomous driving evaluation have been explored, which are usually,
however, based on different simulation platforms, types of safety-critical
scenarios, scenario generation algorithms, and driving route variations. Thus,
despite a large amount of effort in autonomous driving testing, it is still
challenging to compare and understand the effectiveness and efficiency of
different testing scenario generation algorithms and testing mechanisms under
similar conditions. In this paper, we aim to provide the first unified platform
SafeBench to integrate different types of safety-critical testing scenarios,
scenario generation algorithms, and other variations such as driving routes and
environments. Meanwhile, we implement 4 deep reinforcement learning-based AD
algorithms with 4 types of input (e.g., bird's-eye view, camera) to perform
fair comparisons on SafeBench. We find our generated testing scenarios are
indeed more challenging and observe the trade-off between the performance of AD
agents under benign and safety-critical testing scenarios. We believe our
unified platform SafeBench for large-scale and effective autonomous driving
testing will motivate the development of new testing scenario generation and
safe AD algorithms. SafeBench is available at https://safebench.github.io
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