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Found 26 papers, 10 papers with code
HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
The proposed Historical Prediction Attention together with the Agent Attention and Mode Attention is further formulated as the Triple Factorized Attention module, serving as the core design of HPNet. Experiments on the Argoverse and INTERACTION datasets show that HPNet achieves state-of-the-art performance, and generates accurate and stable future trajectories.
Triplet Knowledge Distillation
Distillation then begins in an online manner, and the teacher is only allowed to express solutions within the aforementioned subspace.
Function-Consistent Feature Distillation
The core idea of FCFD is to make teacher and student features not only numerically similar, but more importantly produce similar outputs when fed to the later part of the same network.
DandelionNet: Domain Composition with Instance Adaptive Classification for Domain Generalization
Domain generalization (DG) attempts to learn a model on source domains that can well generalize to unseen but different domains.
Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation
Firstly, we introduce a class-aware cross entropy (CCE) loss for network training.
EigenGAN: Layer-Wise Eigen-Learning for GANs
Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations.
PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing
Facial attribute editing aims to manipulate attributes on the human face, e. g., adding a mustache or changing the hair color.
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
This regularized CAM can be embedded in most recent advanced weakly supervised semantic segmentation framework.
Weakly Supervised Object Detection with Segmentation Collaboration
Weakly supervised object detection aims at learning precise object detectors, given image category labels.
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks
Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e. g. smartphone, embedded devices, etc.
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.
Generative Adversarial Network with Spatial Attention for Face Attribute Editing
The generator contains an attribute manipulation network (AMN) to edit the face image, and a spatial attention network (SAN) to localize the attribute-specific region which restricts the alternation of AMN within this region.
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation
Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation.
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.
AttGAN: Facial Attribute Editing by Only Changing What You Want
Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes.
Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition
The designed ReST has an intrinsic recursive structure and is capable of progressively aligning faces to a canonical one, even those with large variations.
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.
VIPLFaceNet: An Open Source Deep Face Recognition SDK
Robust face representation is imperative to highly accurate face recognition.
Occlusion-Free Face Alignment: Deep Regression Networks Coupled With De-Corrupt AutoEncoders
Face alignment or facial landmark detection plays an important role in many computer vision applications, e. g., face recognition, facial expression recognition, face animation, etc.
Multi-View Deep Network for Cross-View Classification
As a result, the representation from the topmost layers of the MvDN network is robust to view discrepancy, and also discriminative.
Leveraging Datasets With Varying Annotations for Face Alignment via Deep Regression Network
Facial landmark detection, as a vital topic in computer vision, has been studied for many decades and lots of datasets have been collected for evaluation.
Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation
To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE).
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
Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses
Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity.
