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Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
XiangLi, WenhaiWang, XiaolinHu....
Published date-11/25/2020
DenseObjectDetection, ObjectClassification, ObjectDetection
Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and …
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
PengSun, JiechaoXiong, LeiHan....
Published date-11/25/2020
Dota2, Multi-agentReinforcementLearning, Starcraft, StarcraftII
Competitive Self-Play (CSP) based Multi-Agent Reinforcement Learning (MARL) has shown phenomenal breakthroughs recently. Strong AIs are achieved for several benchmarks, including Dota 2, Glory of Kings, Quake III, StarCraft II, …
The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability
SunipaDev....
Published date-11/25/2020
KnowledgeGraphs
High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of …
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
TianyuHan, SvenNebelung, FedericoPedersoli....
Published date-11/25/2020
DecisionMaking
Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability …
Fast Region Proposal Learning for Object Detection for Robotics
FedericoCeola, ElisaMaiettini, GiuliaPasquale....
Published date-11/25/2020
ObjectDetection, RegionProposal
Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable performance. Unfortunately, training such …
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
PeizeSun, RufengZhang, YiJiang....
Published date-11/25/2020
ObjectDetection, ObjectRecognition
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as $k$ anchor boxes pre-defined …