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Found 18 papers, 4 papers with code
PoseGen: Learning to Generate 3D Human Pose Dataset with NeRF
The objective of PoseGen is to learn a distribution of data that maximizes the prediction error of a given pre-trained model.
Automatic Labeling of Parkinson’s Disease Gait Videos with Weak Supervision
The method obtained state-of-the-art results on the Human3. 6M dataset.
Multi-modal Streaming 3D Object Detection
Recent streaming perception works proposed directly processing LiDAR slices and compensating for the narrow field of view (FOV) of a slice by reusing features from preceding slices.
Multi-Channel Vision Transformer for Epileptic Seizure Prediction
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness.
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation
To this end, we propose AdaptPose, an end-to-end framework that generates synthetic 3D human motions from a source dataset and uses them to fine-tune a 3D pose estimator.
TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video
Estimating 3D human poses from video is a challenging problem.
Ranked #13 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
Multi-view 3D Reconstruction with Transformer
Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network.
Ranked #5 on
3D Reconstruction
on ShapeNet
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection
The dense RGB input contributed more to the success of the adversarial attacks on both cascaded and fusion models.
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models
We attack a prominent cascaded multi-modal DNN, the Frustum-Pointnet model.
Multi-View 3D Reconstruction With Transformers
Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network.
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification
White-box adversarial attacks can fool neural networks with small adversarial perturbations, especially for large size images.
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection
Specially, when adversaries consider imperceptibility as a constraint, the proposed anti-forensic method can improve the average attack success rate by around 30\% on fake face images over two baseline attacks.
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture
Accurate prediction of epileptic seizures has remained elusive, despite the many advances in machine learning and time-series classification.
Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals
In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals.
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
2) The traditional PCA is not a reliable method for iEEG data reduction in seizure prediction.
DT-LET: Deep Transfer Learning by Exploring where to Transfer
Previous transfer learning methods based on deep network assume the knowledge should be transferred between the same hidden layers of the source domain and the target domains.
Distributed Compressive Sensing: A Deep Learning Approach
As the proposed method is a data driven method, it is only applicable when training data is available.
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
The results show that the proposed method in this paper significantly outperforms it for web document retrieval task.
