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Multi-Agent Active Search using Realistic Depth-Aware Noise Model
RaminaGhods, WilliamJ.Durkin, JeffSchneider....
Published date-11/09/2020
ObjectDetection
The search for objects of interest in an unknown environment by making data-collection decisions (i.e., active search or active sensing) has robotics applications in many fields, including the search and …
EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach
YannickBukschat, MarcusVetter....
Published date-11/09/2020
2DObjectDetection, 6DPoseEstimation, 6DPoseEstimationusingRGB, ObjectDetection, PoseEstimation
In this paper we introduce EfficientPose, a new approach for 6D object pose estimation. Our method is highly accurate, efficient and scalable over a wide range of computational resources. Moreover, …
PAMS: Quantized Super-Resolution via Parameterized Max Scale
HuixiaLi, ChenqianYan, ShaohuiLin....
Published date-11/09/2020
Quantization, SuperResolution, Super-Resolution, TransferLearning
Deep convolutional neural networks (DCNNs) have shown dominant performance in the task of super-resolution (SR). However, their heavy memory cost and computation overhead significantly restrict their practical deployments on resource-limited …
Adversarial Semantic Collisions
CongzhengSong, AlexanderM.Rush, VitalyShmatikov....
Published date-11/09/2020
ParaphraseIdentification
We study semantic collisions: texts that are semantically unrelated but judged as similar by NLP models. We develop gradient-based approaches for generating semantic collisions and demonstrate that state-of-the-art models for …
Privacy-Preserving XGBoost Inference
XianruiMeng, JoanFeigenbaum....
Published date-11/09/2020
Although machine learning (ML) is widely used for predictive tasks, there are important scenarios in which ML cannot be used or at least cannot achieve its full potential. A major …
Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation
ErikStammes, TomF.H.Runia, MichaelHofmann....
Published date-11/09/2020
SemanticSegmentation, Weakly-SupervisedSemanticSegmentation
Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain. Recent advancements in the weakly-supervised setting show …