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Found 8 papers, 2 papers with code
Collaboratively Self-supervised Video Representation Learning for Action Recognition
Considering the close connection between action recognition and human pose estimation, we design a Collaboratively Self-supervised Video Representation (CSVR) learning framework specific to action recognition by jointly considering generative pose prediction and discriminative context matching as pretext tasks.
Hierarchical Compositional Representations for Few-shot Action Recognition
Recently action recognition has received more and more attention for its comprehensive and practical applications in intelligent surveillance and human-computer interaction.
Attribute Group Editing for Reliable Few-shot Image Generation
Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs).
Meta-Learning with Individualized Feature Space for Few-Shot Classification
Specifically, we introduce a kernel generator as meta-learner to learn to construct feature embedding for query images.
Face Recognition with Contrastive Convolution
In current face recognition approaches with convolutional neural network (CNN), a pair of faces to compare are independently fed into the CNN for feature extraction.
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
Rotation-invariant face detection, i. e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances.
Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness
On the other hand, by using a unified MLP cascade to examine proposals of all views in a centralized style, it provides a favorable solution for multi-view face detection with high accuracy and low time-cost.
AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation
Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme.
Ranked #4 on
Age Estimation
on ChaLearn 2015
