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Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
NanboLi, CE, RobertFisher....
Published date-12/01/2020
SceneUnderstanding
Learning object-centric representations of multi-object scenes is a promising approach towards machine intelligence, facilitating high-level reasoning and control from visual sensory data. However, current approaches for \textit{unsupervised object-centric scene representation} …
Information Maximization for Few-Shot Learning
MalikBoudiaf, ImtiazZiko, JérômeRony....
Published date-12/01/2020
Few-ShotLearning, Meta-Learning
We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task, in conjunction …
Counting People by Estimating People Flows
WeizheLiu, MathieuSalzmann, PascalFua....
Published date-12/01/2020
ActiveLearning, CrowdCounting, OpticalFlowEstimation
Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in …
Fully Convolutional Networks for Panoptic Segmentation
YanweiLi, HengshuangZhao, XiaojuanQi....
Published date-12/01/2020
PanopticSegmentation
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff …
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
JiaxingWang, HaoliBai, JiaxiangWu....
Published date-12/01/2020
NeuralArchitectureSearch
Recent advances in neural architecture search inspire many channel number search algorithms~(CNS) for convolutional neural networks. To improve searching efficiency, parameter sharing is widely applied, which reuses parameters among different …
Model Class Reliance for Random Forests
GavinSmith, RobertoMansilla, JamesGoulding....
Published date-12/01/2020
Variable Importance (VI) has traditionally been cast as the process of estimating each variables contribution to a predictive model's overall performance. Analysis of a single model instance, however, guarantees no …