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Found 13 papers, 5 papers with code
Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation
In this work, we propose a novel feature enhancement network to simultaneously model short- and long-term temporal correlation.
Conformal Off-policy Prediction
Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment.
KRNet: Towards Efficient Knowledge Replay
However, the number of stored latent codes in autoencoder increases linearly with the scale of data and the trained encoder is redundant for the replaying stage.
Ridgeless Regression with Random Features
Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization.
NetRCA: An Effective Network Fault Cause Localization Algorithm
Localizing the root cause of network faults is crucial to network operation and maintenance.
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms
As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.
EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography
In this paper, we present an anchor-free ellipse detection network, namely EllipseNet, which detects the cardiac and thoracic regions in ellipse and automatically calculates the CTR and cardiac axis for fetal cardiac biometrics in 4-chamber view.
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples.
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.
Fast Task Adaptation for Few-Shot Learning
The key lies in generalization of prior knowledge learned from large-scale base classes and fast adaptation of the classifier to novel classes.
Learning Incremental Triplet Margin for Person Re-identification
In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively.
Cascade Region Proposal and Global Context for Deep Object Detection
Deep region-based object detector consists of a region proposal step and a deep object recognition step.
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map.
Ranked #5 on
Crowd Counting
on Venice
