Search Results for author:
Found 26 papers, 10 papers with code
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization
In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization.
Boosted Dynamic Neural Networks
To optimize the model, these prediction heads together with the network backbone are trained on every batch of training data.
Implicit Autoencoder for Point Cloud Self-supervised Representation Learning
The most popular and accessible 3D representation, i. e., point clouds, involves discrete samples of the underlying continuous 3D surface.
Ranked #3 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
3D Point Cloud Classification
3D Point Cloud Linear Classification
+3
Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning
Methods: The proposed framework consists of a reconstruction module and a generative module.
Weakly-guided Self-supervised Pretraining for Temporal Activity Detection
However, such pretrained models are not ideal for downstream detection, due to the disparity between the pretraining and the downstream fine-tuning tasks.
Ranked #3 on
Action Detection
on Charades
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion.
Multi-Person Pose Estimation
Multi-Person Pose Estimation and Tracking
+1
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition
It is shown that, unlike class-balanced sampling, this is an adversarial augmentation strategy.
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition
A new learning algorithm is then proposed for GeometrIc Structure Transfer (GIST), with resort to a combination of loss functions that combine class-balanced and random sampling to guarantee that, while overfitting to the popular classes is restricted to geometric parameters, it is leveraged to transfer class geometry from popular to few-shot classes.
Semi-supervised Long-tailed Recognition using Alternate Sampling
A consistency loss has been introduced to limit the impact from unlabeled data while leveraging them to update the feature embedding.
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas
By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible.
Few-Shot Open-Set Recognition using Meta-Learning
It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes.
Any-Precision Deep Neural Networks
When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision.
Towards Physically Safe Reinforcement Learning under Supervision
A weighted average of the supervisor and learned policies is used during trials, with a heavier weight initially on the supervisor, in order to allow safe and useful physical trials while the learned policy is still ineffective.
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias
While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts.
Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots
We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs.
Deep Face Detector Adaptation Without Negative Transfer or Catastrophic Forgetting
Arguably, no single face detector fits all real-life scenarios.
Towards Interpretable Face Recognition
Deep CNNs have been pushing the frontier of visual recognition over past years.
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation
Synthetic data suffers from domain gap to the real-world scenes while visual inputs rendered from 3D reconstructed scenes have undesired holes and artifacts.
Robotics
Learning Dense Facial Correspondences in Unconstrained Images
To train such a network, we generate a massive dataset of synthetic faces with dense labels using renderings of a morphable face model with variations in pose, expressions, lighting, and occlusions.
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Many seemingly distant annotations (e. g., semantic segmentation and visual question answering (VQA)) are inherently connected in that they reveal different levels and perspectives of human understandings about the same visual scenes --- and even the same set of images (e. g., of COCO).
A Multi-Level Contextual Model For Person Recognition in Photo Albums
Our new framework enables efficient use of these complementary multi-level contextual cues to improve overall recognition rates on the photo album person recognition task, as demonstrated through state-of-the-art results on a challenging public dataset.
A Convolutional Neural Network Cascade for Face Detection
To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade.
Hierarchical-PEP Model for Real-World Face Recognition
We apply the PEP model hierarchically to decompose a face image into face parts at different levels of details to build pose-invariant part-based face representations.
Efficient Boosted Exemplar-based Face Detection
Despite the fact that face detection has been studied intensively over the past several decades, the problem is still not completely solved.
Probabilistic Elastic Matching for Pose Variant Face Verification
By augmenting each feature with its location, a Gaussian mixture model (GMM) is trained to capture the spatialappearance distribution of all face images in the training corpus.
