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Found 29 papers, 7 papers with code
LibFewShot: A Comprehensive Library for Few-shot Learning
Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmark datasets with multiple backbone architectures to evaluate common pitfalls and effects of different training tricks.
Triplet is All You Need with Random Mappings for Unsupervised Visual Representation Learning
However, this type of methods, such as SimCLR and MoCo, relies heavily on a large number of negative pairs and thus requires either large batches or memory banks.
CariMe: Unpaired Caricature Generation with Multiple Exaggerations
Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution for exaggerations in caricatures.
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning
That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via the optimization.
Manifold Alignment for Semantically Aligned Style Transfer
In this paper, we make a new assumption that image features from the same semantic region form a manifold and an image with multiple semantic regions follows a multi-manifold distribution.
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
Inspired by the recent success of Few-Shot Learning (FSL) in natural image classification, we propose to apply FSL to skin disease identification to address the extreme scarcity of training sample problem.
RGBD-Dog: Predicting Canine Pose from RGBD Sensors
We evaluate our model on both synthetic and real RGBD images and compare our results to previously published work fitting canine models to images.
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Importantly, we highlight the value and importance of the distribution diversity in the augmentation-based pretext few-shot tasks, which can effectively alleviate the overfitting problem and make the few-shot model learn more robust feature representations.
Asymmetric Distribution Measure for Few-shot Learning
Given the natural asymmetric relation between a query image and a support class, we argue that an asymmetric measure is more suitable for metric-based few-shot learning.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.
Defensive Few-shot Adversarial Learning
In this paper, instead of assuming such a distribution consistency, we propose to make this assumption at a task-level in the episodic training paradigm in order to better transfer the defense knowledge.
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
Its key difference from the literature is the replacement of the image-level feature based measure in the final layer by a local descriptor based image-to-class measure.
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene.
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning
Furthermore, an attention mechanism is introduced to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated.
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM).
Online Progressive Deep Metric Learning
Furthermore, in a progressively and nonlinearly learning way, ODML has a stronger learning ability than traditional shallow online metric learning in the case of limited available training data.
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
We created a synthetic block stacking environment with physics simulation in which the agent can learn a policy end-to-end through trial and error.
Learn to Model Motion from Blurry Footages
It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects.
WebCaricature: a benchmark for caricature recognition
In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition.
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper.
Visual Stability Prediction and Its Application to Manipulation
We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers.
To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction
In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance.
Nonrigid Optical Flow Ground Truth for Real-World Scenes with Time-Varying Shading Effects
In this paper we present a dense ground truth dataset of nonrigidly deforming real-world scenes.
Video Interpolation using Optical Flow and Laplacian Smoothness
Non-rigid video interpolation is a common computer vision task.
Blur Robust Optical Flow using Motion Channel
It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur.
Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences
We demonstrate the success of our approach by showing significant error reduction on 6 popular optical flow algorithms applied to a range of real-world nonrigid benchmarks.
Learning Multi-Scale Representations for Material Classification
The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors.
Optical Flow Estimation Using Laplacian Mesh Energy
In this paper we present a novel non-rigid optical flow algorithm for dense image correspondence and non-rigid registration.