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Found 13 papers, 5 papers with code
Skeleton-based Action Recognition with Convolutional Neural Networks
Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN).
Ranked #3 on
Skeleton Based Action Recognition
on PKU-MMD
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.
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets.
Ranked #1 on
RF-based Pose Estimation
on RF-MMD
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.
Collaborative Spatio-temporal Feature Learning for Video Action Recognition
By sharing the convolution kernels of different views, spatial and temporal features are collaboratively learned and thus benefit from each other.
Action Recognition In Videos
Temporal Action Localization
+1
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.
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
The key lies in the design of the graph structure, which encodes skeleton topology information.
Modulating Localization and Classification for Harmonized Object Detection
Object detection involves two sub-tasks, i. e. localizing objects in an image and classifying them into various categories.
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.
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition
In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations.
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.
Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning
The knowledge extracted by the delegator is then utilized to maintain the performance of the model on old tasks in incremental learning.
